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Section 59

EurekaMag Full Text Articles Chapter 58,594



References:

Basques, B.A.; Ibe, I.; Samuel, A.M.; Lukasiewicz, A.M.; Webb, M.L.; Bohl, D.D.; Grauer, J.N. 2017: Predicting Postoperative Morbidity and Readmission for Revision Posterior Lumbar Fusion. Clinical Spine Surgery 30(6): E770-E775
Sieberg, C.B.; Manganella, J.; Manalo, G.; Simons, L.E.; Hresko, M.T. 2017: Predicting Postsurgical Satisfaction in Adolescents with Idiopathic Scoliosis: the Role of Presurgical Functioning and Expectations. Journal of Pediatric Orthopedics 37(8): E548-E551
Bautista, I.J.; Chirosa, I.J.; Tamayo, I.M.ín.; González, A.és.; Robinson, J.E.; Chirosa, L.J.; Robertson, R.J. 2014: Predicting Power Output of Upper Body using the OMNI-RES Scale. Journal of Human Kinetics 44: 161-169
Mehra, L.K.; Cowger, C.; Gross, K.; Ojiambo, P.S. 2016: Predicting Pre-planting Risk of Stagonospora nodorum blotch in Winter Wheat Using Machine Learning Models. Frontiers in Plant Science 7: 390
Tanaka, N.; Ohno, Y.; Hori, M.; Utada, M.; Ito, K.; Suzuki, T.; Furukawa, F. 2015: Predicting Preoperative Hemodynamic Changes Using the Visual Analog Scale. Journal of Perianesthesia Nursing: Official Journal of the American Society of Perianesthesia Nurses 30(6): 460-467
Miller, N.; Frankenfield, D.; Lehman, E.; Maguire, M.; Schirm, V. 2016: Predicting Pressure Ulcer Development in Clinical Practice: Evaluation of Braden Scale Scores and Nutrition Parameters. Journal of Wound Ostomy and Continence Nursing: Official Publication of Wound Ostomy and Continence Nurses Society 43(2): 133-139
Tucker, C.M.; Berrien, K.; Menard, M.Kathryn.; Herring, A.H.; Daniels, J.; Rowley, D.L.; Halpern, C.Tucker. 2015: Predicting Preterm Birth Among Women Screened by North Carolina's Pregnancy Medical Home Program. Maternal and Child Health Journal 19(11): 2438-2452
Georgiou, H.M.; Di Quinzio, M.K.W.; Permezel, M.; Brennecke, S.P. 2015: Predicting Preterm Labour: Current Status and Future Prospects. Disease Markers 2015: 435014
Eggers, S.M.; Mathews, C.; Aarø, L.E.; McClinton-Appollis, T.; Bos, A.E.R.; de Vries, H. 2017: Predicting Primary and Secondary Abstinence Among Adolescent Boys and Girls in the Western Cape, South Africa. Aids and Behavior 21(5): 1417-1428
Dyrba, M.; Barkhof, F.; Fellgiebel, A.; Filippi, M.; Hausner, L.; Hauenstein, K.; Kirste, T.; Teipel, S.J. 2015: Predicting Prodromal Alzheimer's Disease in Subjects with Mild Cognitive Impairment Using Machine Learning Classification of Multimodal Multicenter Diffusion-Tensor and Magnetic Resonance Imaging Data. Journal of Neuroimaging: Official Journal of the American Society of Neuroimaging 25(5): 738-747
Lauer, M.S.; Danthi, N.S.; Kaltman, J.; Wu, C. 2015: Predicting Productivity Returns on Investment: Thirty Years of Peer Review, Grant Funding, and Publication of Highly Cited Papers at the National Heart, Lung, and Blood Institute. Circulation research 117(3): 239-243
Condon, J.V.; Barefield, A.C. 2016: Predicting Professional Examination Outcomes: a Case of the Registered Health Information Administration Certification Examination. Journal of Allied Health 45(4): 267-273
Waters, D.D.; Arsenault, B.J. 2016: Predicting Prognosis in Acute Coronary Syndromes: Makeover time for TIMi and GRACE?. Canadian Journal of Cardiology 32(11): 1290-1293
Nabhan, C.; Raca, G.; Wang, Y.L. 2015: Predicting Prognosis in Chronic Lymphocytic Leukemia in the Contemporary Era. JAMA Oncology 1(7): 965-974
Der, S.; Zhu, C.-Q.; Brower, S.; Uihlein, A. 2015: Predicting Prognosis of Early-Stage Non-Small Cell Lung Cancer Using the GeneFx® Lung Signature. Plos Currents 7
Korolev, I.O.; Symonds, L.L.; Bozoki, A.C. 2016: Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical, MRI, and Plasma Biomarkers via Probabilistic Pattern Classification. Plos one 11(2): E0138866
Kadatz, M.J.; Lee, E.S.; Levin, A. 2016: Predicting Progression in CKD: Perspectives and Precautions. American Journal of Kidney Diseases: the Official Journal of the National Kidney Foundation 67(5): 779-786
Cideciyan, A.V.; Swider, M.; Schwartz, S.B.; Stone, E.M.; Jacobson, S.G. 2015: Predicting Progression of ABCA4-Associated Retinal Degenerations Based on Longitudinal Measurements of the Leading Disease Front. Investigative Ophthalmology and Visual Science 56(10): 5946-5955
Tsai, C.; Latimer, A.A.; Yoo, J.S.; Studt, F.; Abild-Pedersen, F. 2015: Predicting Promoter-Induced Bond Activation on Solid Catalysts Using Elementary Bond Orders. Journal of Physical Chemistry Letters 6(18): 3670-3674
Zhang, K.; Shen, Y.; Zhang, X.; Ma, L.; Wang, H.; An, N.; Guo, A.; Ye, H. 2016: Predicting Prostate Biopsy Outcomes: a Preliminary Investigation on Screening with Ultrahigh B-Value Diffusion-Weighted Imaging as an Innovative Diagnostic Biomarker. Plos one 11(3): E0151176
Albitar, M.; Ma, W.; Lund, L.; Albitar, F.; Diep, K.; Fritsche, H.A.; Shore, N. 2016: Predicting Prostate Biopsy Results Using a Panel of Plasma and Urine Biomarkers Combined in a Scoring System. Journal of Cancer 7(3): 297-303
Jeffers, A.; Sochat, V.; Kattan, M.W.; Yu, C.; Melcon, E.; Yamoah, K.; Rebbeck, T.R.; Whittemore, A.S. 2017: Predicting Prostate Cancer Recurrence After Radical Prostatectomy. Prostate 77(3): 291-298
Yu, G.; Rangwala, H.; Domeniconi, C.; Zhang, G.; Zhang, Z. 2015: Predicting Protein Function Using Multiple Kernels. Ieee/Acm Transactions on Computational Biology and Bioinformatics 12(1): 219-233
Yu, G.; Fu, G.; Wang, J.; Zhu, H. 2016: Predicting Protein Function via Semantic Integration of Multiple Networks. Ieee/Acm Transactions on Computational Biology and Bioinformatics 13(2): 220-232
Deng, X.; Li, J.; Cheng, J. 2013: Predicting Protein Model Quality from Sequence Alignments by Support Vector Machines. Journal of Proteomics and Bioinformatics Suppl. 9
García-Jiménez, B.; Pons, T.; Sanchis, A.; Valencia, A. 2014: Predicting Protein Relationships to Human Pathways through a Relational Learning Approach Based on Simple Sequence Features. Ieee/Acm Transactions on Computational Biology and Bioinformatics 11(4): 753-765
Kandoi, G.; Leelananda, S.P.; Jernigan, R.L.; Sen, T.Z. 2017: Predicting Protein Secondary Structure Using Consensus Data Mining (CDM) Based on Empirical Statistics and Evolutionary Information. Methods in Molecular Biology 1484: 35-44
Hu, J.; Li, Y.; Zhang, M.; Yang, X.; Shen, H-Bin.; Yu, D-Jun. 2017: Predicting Protein-DNA Binding Residues by Weightedly Combining Sequence-Based Features and Boosting Multiple SVMs. Ieee/Acm Transactions on Computational Biology and Bioinformatics 14(6): 1389-1398
Kuo, T.-H.; Li, K.-B. 2016: Predicting Protein-Protein Interaction Sites Using Sequence Descriptors and Site Propensity of Neighboring Amino Acids. International Journal of Molecular Sciences 17(11)
Sriwastava, B.K.; Basu, S.; Maulik, U. 2015: Predicting Protein-Protein Interaction Sites with a Novel Membership Based Fuzzy SVM Classifier. IEEE/ACM transactions on computational biology and bioinformatics 12(6): 1394-1404
Almeida, R.M.; Dell'Acqua, S.; Krippahl, L.; Moura, J.é J.G.; Pauleta, S.R. 2016: Predicting Protein-Protein Interactions Using BiGGER: Case Studies. Molecules 21(8)
Keskin, O.; Tuncbag, N.; Gursoy, A. 2016: Predicting Protein-Protein Interactions from the Molecular to the Proteome Level. Chemical Reviews 116(8): 4884-4909
Paschoal, D.; Guerra, C.F.; de Oliveira, M.A.L.; Ramalho, T.C.; Dos Santos, H.F. 2016: Predicting Pt-195 NMR chemical shift using new relativistic all-electron basis set. Journal of Computational Chemistry 37(26): 2360-2373
Heinzerling, L.; Hartmann, R.W.; Frotscher, M.; Neumann, D. 2010: Predicting Putative Inhibitors of 17β-HSD1. Molecular Informatics 29(10): 695-705
Herrera, S.; Reyes-Herrera, P.H.; Shank, T.M. 2015: Predicting RAD-seq Marker Numbers across the Eukaryotic Tree of Life. Genome Biology and Evolution 7(12): 3207-3225
Kerpedjiev, P.; Höner Zu Siederdissen, C.; Hofacker, I.L. 2015: Predicting RNA 3D structure using a coarse-grain helix-centered model. Rna 21(6): 1110-1121
Sakuraba, S.; Asai, K.; Kameda, T. 2015: Predicting RNA Duplex Dimerization Free-Energy Changes upon Mutations Using Molecular Dynamics Simulations. Journal of Physical Chemistry Letters 6(21): 4348-4351
Lorenz, R.; Wolfinger, M.T.; Tanzer, A.; Hofacker, I.L. 2016: Predicting RNA secondary structures from sequence and probing data. Methods 103: 86-98
DiChiacchio, L.; Mathews, D.H. 2016: Predicting RNA-RNA Interactions Using RNAstructure. Methods in Molecular Biology 1490: 51-62
Mehmood, Q.; Sun, A.; Becker, N.; Higgins, J.; Marshall, A.; Le, L.W.; Vines, D.C.; McCloskey, P.; Ford, V.; Clarke, K.; Yap, M.; Bezjak, A.; Bissonnette, J.-P. 2016: Predicting Radiation Esophagitis Using 18F-FDG PET During Chemoradiotherapy for Locally Advanced Non-Small Cell Lung Cancer. Journal of Thoracic Oncology: Official Publication of the International Association for the Study of Lung Cancer 11(2): 213-221
Van Oorschot, B.; Hovingh, S.; Dekker, A.; Stalpers, L.J.; Franken, N.A.P. 2016: Predicting Radiosensitivity with Gamma-H2AX Foci Assay after Single High-Dose-Rate and Pulsed Dose-Rate Ionizing Irradiation. Radiation Research 185(2): 190-198
Feng, Z.; Xu, Q.S.; Qin, L.Z.; Li, H.; Han, Z. 2017: Predicting Radiotherapy Necessity in Tongue Cancer Using Lymph Node Yield. Journal of Oral and Maxillofacial Surgery: Official Journal of the American Association of Oral and Maxillofacial Surgeons 75(5): 1062-1070
Chaudhuri, A.A.; Binkley, M.S.; Osmundson, E.C.; Alizadeh, A.A.; Diehn, M. 2015: Predicting Radiotherapy Responses and Treatment Outcomes Through Analysis of Circulating Tumor DNA. Seminars in Radiation Oncology 25(4): 305-312
Osman, S.L. 2016: Predicting Rape Victim Empathy Based on Rape Victimization and Acknowledgment Labeling. Violence Against Women 22(7): 767-779
AbdulHameed, M.D.M.; Ippolito, D.L.; Wallqvist, A. 2016: Predicting Rat and Human Pregnane X Receptor Activators Using Bayesian Classification Models. Chemical Research in Toxicology 29(10): 1729-1740
Messner, S.F.; Liu, J.; Zhao, Y. 2018: Predicting Re-Incarceration Status of Prisoners in Contemporary China: Applying Western Criminological Theories. International Journal of Offender Therapy and Comparative Criminology 62(4): 1018-1042
Zhao, M.; Anderson, A.B. 2016: Predicting Reaction Mechanisms and Potentials in Acid and Base from Self-Consistent Quantum Theory: H(ads) and OH(ads) Deposition on the Pt(111) Electrode. Journal of Physical Chemistry Letters 7(4): 711-714
Michaels, J.A.; Dann, B.; Intveld, R.W.; Scherberger, H.ör. 2015: Predicting Reaction time from the Neural State Space of the Premotor and Parietal Grasping Network. Journal of Neuroscience: the Official Journal of the Society for Neuroscience 35(32): 11415-11432
Bigozzi, L.; Tarchi, C.; Caudek, C.; Pinto, G. 2016: Predicting Reading and Spelling Disorders: a 4-Year Prospective Cohort Study. Frontiers in Psychology 7: 337
Pinto, G.; Bigozzi, L.; Tarchi, C.; Vezzani, C.; Accorti Gamannossi, B. 2016: Predicting Reading, Spelling, and Mathematical Skills: a Longitudinal Study from Kindergarten Through first Grade. Psychological Reports 118(2): 413-440
Tabak, Y.P.; Sun, X.; Nunez, C.M.; Gupta, V.; Johannes, R.S. 2017: Predicting Readmission at Early Hospitalization Using Electronic Clinical Data: An Early Readmission Risk Score. Medical Care 55(3): 267-275
Peterson, L.; Jamroz, M.; Kolinski, A.; Kihara, D. 2017: Predicting Real-Valued Protein Residue Fluctuation Using FlexPred. Methods in Molecular Biology 1484: 175-186
Tesoriero, A.J.; Terziotti, S.; Abrams, D.B. 2015: Predicting Redox Conditions in Groundwater at a Regional Scale. Environmental Science and Technology 49(16): 9657-9664
Lowe, J.A.; Crist, B.D.; Pfeiffer, F.; Carson, W.L. 2015: Predicting Reduction in Torsional Strength by Concentric/Eccentric RIA Reaming Normal and Osteoporotic Long Bones (Femurs). Journal of Orthopaedic Trauma 29(10): E371-E379
Mattsson, N.; Insel, P.S.; Donohue, M.; Jagust, W.; Sperling, R.; Aisen, P.; Weiner, M.W. 2015: Predicting Reduction of Cerebrospinal Fluid β-Amyloid 42 in Cognitively Healthy Controls. JAMA Neurology 72(5): 554-560
Almquist, Z.W.; Butts, C.T. 2015: Predicting Regional Self-identification from Spatial Network Models. Geographical Analysis 47(1): 50-72
Tyzack, J.D.; Hunt, P.A.; Segall, M.D. 2016: Predicting Regioselectivity and Lability of Cytochrome P450 Metabolism Using Quantum Mechanical Simulations. Journal of Chemical Information and Modeling 56(11): 2180-2193
Almutairi, K.M. 2016: Predicting Relationship of Smoking Behavior Among Male Saudi Arabian College Students Related to Their Religious Practice. Journal of religion and health 55(2): 469-479
Norouzi, J.; Yadollahpour, A.; Mirbagheri, S.A.; Mazdeh, M.M.; Hosseini, S.A. 2016: Predicting Renal Failure Progression in Chronic Kidney Disease Using Integrated Intelligent Fuzzy Expert System. Computational and Mathematical Methods in Medicine 2016: 6080814
Meyer, A.; Woldu, S.L.; Weinberg, A.C.; Thoreson, G.R.; Pierorazio, P.; Matulay, J.T.; Benson, M.C.; DeCastro, G.J.; McKiernan, J.M. 2015: Predicting Renal Parenchymal Loss after Nephron Sparing Surgery. Journal of Urology 194(3): 658-663
Hawkins, N.J.; Fraaije, B.A. 2016: Predicting Resistance by Mutagenesis: Lessons from 45 Years of MBC Resistance. Frontiers in microbiology 7: 1814
Segal, O.; Barayev, E.; Nemet, A.Y.; Mimouni, M. 2016: Predicting Response of Exudative Age-Related Macular Degeneration to Bevacizumab Based on Spectralis Optical Coherence Tomography. Retina 36(2): 259-263
Santacana, Mí.; Arias, Bárbara.; Mitjans, M.; Bonillo, A.; Montoro, Mía.; Rosado, Sílvia.; Guillamat, R.; Vallès, Vç.; Pérez, Víctor.; Forero, C.G.; Fullana, M.A. 2016: Predicting Response Trajectories during Cognitive-Behavioural Therapy for Panic Disorder: No Association with the BDNF Gene or Childhood Maltreatment. Plos one 11(6): E0158224
Geeleher, P.; Loboda, A.; Lenkala, D.; Wang, F.; LaCroix, B.; Karovic, S.; Wang, J.; Nebozhyn, M.; Chisamore, M.; Hardwick, J.; Maitland, M.L.; Huang, R.S. 2015: Predicting Response to Histone Deacetylase Inhibitors Using High-Throughput Genomics. Journal of the National Cancer Institute 107(11)
Zuiverloon, T.C.M.; Zwarthoff, E.C. 2016: Predicting Response to Intravesical Bacillus Calmette-Guérin Immunotherapy: Are we Moving Forward?. European Urology 69(2): 201-202
Ypsilantis, P.-P.; Siddique, M.; Sohn, H.-M.; Davies, A.; Cook, G.; Goh, V.; Montana, G. 2015: Predicting Response to Neoadjuvant Chemotherapy with PET Imaging Using Convolutional Neural Networks. Plos one 10(9): E0137036
Gammaitoni, A.R.; Trudeau, J.J.; Radnovich, R.; Galer, B.S.; Jensen, M.P. 2015: Predicting Response to Subacromial Injections and Lidocaine/Tetracaine Patch from Pretreatment Pain Quality in Patients with Shoulder Impingement Syndrome. Pain Medicine 16(7): 1333-1340
Hill, D.A.; Lim, H.-W.; Kim, Y.H.; Ho, W.Y.; Foong, Y.H.; Nelson, V.L.; Nguyen, H.C.B.; Chegireddy, K.; Kim, J.; Habertheuer, A.; Vallabhajosyula, P.; Kambayashi, T.; Won, K.-J.; Lazar, M.A. 2018: Distinct macrophage populations direct inflammatory versus physiological changes in adipose tissue. Proceedings of the National Academy of Sciences of the United States of America 115(22): E5096-E5105
Zhang, J.; Zhang, Z.; Zhang, Y.; Wu, Y. 2018: Distinct magnetic resonance imaging features in a patient with novel RARS2 mutations: A case report and review of the literature. Experimental and Therapeutic Medicine 15(1): 1099-1104
Akbulut, V.; Weger, H. 2016: Predicting Responses to Bids for Sexual and Romantic Escalation in Cross-Sex Friendships. Journal of Social Psychology 156(1): 98-114
Tromberg, B.J.; Zhang, Z.; Leproux, A.ïs.; O'Sullivan, T.D.; Cerussi, A.E.; Carpenter, P.M.; Mehta, R.S.; Roblyer, D.; Yang, W.; Paulsen, K.D.; Pogue, B.W.; Jiang, S.; Kaufman, P.A.; Yodh, A.G.; Chung, S.H.; Schnall, M.; Snyder, B.S.; Hylton, N.; Boas, D.A.; Carp, S.A.; Isakoff, S.J.; Mankoff, D. 2016: Predicting Responses to Neoadjuvant Chemotherapy in Breast Cancer: ACRIN 6691 Trial of Diffuse Optical Spectroscopic Imaging. Cancer Research 76(20): 5933-5944
Nozaki, T.; Tasaki, A.; Horiuchi, S.; Ochi, J.; Starkey, J.; Hara, T.; Saida, Y.; Yoshioka, H. 2016: Predicting Retear after Repair of Full-Thickness Rotator Cuff Tear: Two-Point Dixon MR Imaging Quantification of Fatty Muscle Degeneration-Initial Experience with 1-year Follow-up. Radiology 280(2): 500-509
Martin, D.M.; Gálvez, V.òn.; Loo, C.K. 2015: Predicting Retrograde Autobiographical Memory Changes Following Electroconvulsive Therapy: Relationships between Individual, Treatment, and Early Clinical Factors. International Journal of Neuropsychopharmacology 18(12)
Kim, E.; Hou, J.; Han, J.Yeob.; Himelboim, I. 2016: Predicting Retweeting Behavior on Breast Cancer Social Networks: Network and Content Characteristics. Journal of Health Communication 21(4): 479-486
Lancellotti, P.; Moonen, M.; Jerusalem, G. 2016: Predicting Reversibility of Anticancer Drugs-Related Cardiac Dysfunction: a new Piece to the Routine use of Deformation Imaging. Echocardiography 33(4): 504-509
Gaspar, M.P.; Kane, P.M.; Putthiwara, D.; Jacoby, S.M.; Osterman, A.Lee. 2016: Predicting Revision Following In Situ Ulnar Nerve Decompression for Patients With Idiopathic Cubital Tunnel Syndrome. Journal of Hand Surgery 41(3): 427-435
Guo, H. 2017: Predicting Rights-Only Score Distributions from Data Collected Under Formula Score Instructions. Psychometrika 82(1): 1-16
Hachamovitch, R.; Nutter, B.; Menon, V.; Cerqueira, M.D. 2015: Predicting Risk Versus Predicting Potential Survival Benefit Using 123I-mIBG Imaging in Patients with Systolic Dysfunction Eligible for Implantable Cardiac Defibrillator Implantation: Analysis of Data from the Prospective ADMIRE-HF Study. CIRCULATION. Cardiovascular Imaging 8(12)
Seimiya, H. 2015: Predicting Risk at the End of the End: Telomere G-tail as a Biomarker. Ebiomedicine 2(8): 804-805
Brackman, E.H.; Morris, B.W.; Andover, M.S. 2016: Predicting Risk for Suicide: A Preliminary Examination of Non-Suicidal Self-Injury and the Acquired Capability Construct in a College Sample. Archives of suicide research: official journal of the International Academy for Suicide Research 20(4): 663-676
Ramirez, M.E.; Bargman, J. 2015: Predicting Risk in Peritoneal Dialysis: Is Membrane Biology Destiny?. Clinical Journal of the American Society of Nephrology: Cjasn 10(11): 1895-1896
Odeh, R.; Noone, D.; Bowlin, P.R.; Braga, L.H.P.; Lorenzo, A.J. 2016: Predicting Risk of Chronic Kidney Disease in Infants and Young Children at Diagnosis of Posterior Urethral Valves: Initial Ultrasound Kidney Characteristics and Validation of Parenchymal Area as Forecasters of Renal Reserve. Journal of Urology 196(3): 862-868
Ross, S.S. 2016: Predicting Risk of Chronic Renal Disease in Children with Vesicoureteral Reflux-How Good or Bad are we Doing?. Journal of Urology 195(4 Part 1): 829-830
Harrison, S.L.; de Craen, A.J.M.; Kerse, N.; Teh, R.; Granic, A.; Davies, K.; Wesnes, K.A.; den Elzen, W.P.J.; Gussekloo, J.; Kirkwood, T.B.L.; Robinson, L.; Jagger, C.; Siervo, M.; Stephan, B.C.M. 2017: Predicting Risk of Cognitive Decline in very Old Adults Using Three Models: the Framingham Stroke Risk Profile; the Cardiovascular Risk Factors, Aging, and Dementia Model; and Oxi-Inflammatory Biomarkers. Journal of the American Geriatrics Society 65(2): 381-389
Tonolini, M.; Rigiroli, F.; Scorza, D. 2016: Predicting Risk of Contrast-Induced Nephrotoxicity in Hospitalized Patients Undergoing Computed Tomography Using the Mehran Stratification Score. Current Problems in Diagnostic Radiology 45(3): 238-239
Lo Re, V.; Kallan, M.J.; Tate, J.P.; Lim, J.K.; Goetz, M.B.; Klein, M.B.; Rimland, D.; Rodriguez-Barradas, M.C.; Butt, A.A.; Gibert, C.L.; Brown, S.T.; Park, L.S.; Dubrow, R.; Reddy, K.R.; Kostman, J.R.; Justice, A.C.; Localio, A.R. 2015: Predicting Risk of End-Stage Liver Disease in Antiretroviral-Treated Human Immunodeficiency Virus/Hepatitis C Virus-Coinfected Patients. Open Forum Infectious Diseases 2(3): Ofv109
Palraj, B.R.; Baddour, L.M.; Hess, E.P.; Steckelberg, J.M.; Wilson, W.R.; Lahr, B.D.; Sohail, M.R. 2015: Predicting Risk of Endocarditis Using a Clinical Tool (PREDICT): Scoring System to Guide use of Echocardiography in the Management of Staphylococcus aureus Bacteremia. Clinical Infectious Diseases: An Official Publication of the Infectious Diseases Society of America 61(1): 18-28
Mehran, R.; Baber, U. 2016: Predicting Risk of Ischemic or Bleeding Events After Percutaneous Coronary Intervention. JAMA Cardiology 1(6): 731-732
Gracitelli, C.P.B.; Tatham, A.J.; Boer, E.R.; Abe, R.Y.; Diniz-Filho, A.; Rosen, P.N.; Medeiros, F.A. 2015: Predicting Risk of Motor Vehicle Collisions in Patients with Glaucoma: a Longitudinal Study. Plos one 10(10): E0138288
Karmakar, C.; Luo, W.; Tran, T.; Berk, M.; Venkatesh, S. 2016: Predicting Risk of Suicide Attempt Using History of Physical Illnesses from Electronic Medical Records. Jmir Mental Health 3(3): E19
Jelovsek, J.Eric.; Hill, A.Jolyn.; Chagin, K.M.; Kattan, M.W.; Barber, M.D. 2016: Predicting Risk of Urinary Incontinence and Adverse Events After Midurethral Sling Surgery in Women. Obstetrics and Gynecology 127(2): 330-340
Anderson, S.L.; Zheng, Y.; McMahon, R.J. 2017: Predicting Risky Sexual Behavior: the Unique and Interactive Roles of Childhood Conduct Disorder Symptoms and Callous-Unemotional Traits. Journal of Abnormal Child Psychology 45(6): 1147-1156
Askitas, N. 2016: Predicting Road Conditions with Internet Search. Plos one 11(8): E0162080
Shannon, C.K.; Price, S.S.; Jackson, J. 2016: Predicting Rural Practice and Service to Indigent Patients: Survey of Dental Students Before and After Rural Community Rotations. Journal of Dental Education 80(10): 1180-1187
Krauskopf, A.L.; Knippel, A.J.; Verde, P.E.; Kozlowski, P. 2016: Predicting SGA neonates using first-trimester screening: influence of previous pregnancy's birthweight and PAPP-A MoM. Journal of Maternal-Fetal and Neonatal Medicine: the Official Journal of the European Association of Perinatal Medicine the Federation of Asia and Oceania Perinatal Societies the International Society of Perinatal Obstetricians 29(18): 2962-2967
Pandey, P.; Cao, W.; Wang, Y.; Vaddella, V. 2016: Predicting Salmonella Typhimurium reductions in poultry ground carcasses. Poultry Science 95(11): 2640-2646
Lee, M.; Huang, Y.; Chong, H.C.; Ning, Y.; Lo, N.N.; Yeo, S.J. 2016: Predicting Satisfaction for Unicompartmental Knee Arthroplasty Patients in an Asian Population. Journal of Arthroplasty 31(8): 1706-1710
Kiware, S.S.; Corliss, G.; Merrill, S.; Lwetoijera, D.W.; Devine, G.; Majambere, S.; Killeen, G.F. 2015: Predicting Scenarios for Successful Autodissemination of Pyriproxyfen by Malaria Vectors from Their Resting Sites to Aquatic Habitats; Description and Simulation Analysis of a Field-Parameterizable Model. Plos one 10(7): E0131835
Shepperd, J.A.; Emanuel, A.S.; Howell, J.L.; Logan, H.L. 2015: Predicting Scheduling and Attending for an Oral Cancer Examination. Annals of Behavioral Medicine: a Publication of the Society of Behavioral Medicine 49(6): 828-838
Keefe, R.S.E.; Reichenberg, A. 2016: Predicting Schizophrenia. JAMA Psychiatry 73(5): 441-442
Hagger, M.S.; Hardcastle, S.J.; Hingley, C.; Strickland, E.; Pang, J.; Watts, G.F. 2016: Predicting Self-Management Behaviors in Familial Hypercholesterolemia Using an Integrated Theoretical Model: the Impact of Beliefs about Illnesses and Beliefs about Behaviors. International Journal of Behavioral Medicine 23(3): 282-294
Böhme, S.; Renneberg, B. 2015: Predicting Self-Rated Health in Diabetes and Chronic Heart Failure - a Multiple Mediation Model. Frontiers in Public Health 3: 266
Thornton, H.R.; Delaney, J.A.; Duthie, G.M.; Scott, B.R.; Chivers, W.J.; Sanctuary, C.E.; Dascombe, B.J. 2016: Predicting Self-Reported Illness for Professional Team-Sport Athletes. International Journal of Sports Physiology and Performance 11(4): 543-550
Schlegl, T.; Waldstein, S.M.; Vogl, W.-D.; Schmidt-Erfurth, U.; Langs, G. 2015: Predicting Semantic Descriptions from Medical Images with Convolutional Neural Networks. Information Processing in Medical Imaging: Proceedings of the . Conference 24: 437-448
Olenick, E.M.; Zimbro, K.S.; DʼLima, G.M.; Ver Schneider, P.; Jones, D. 2017: Predicting Sepsis Risk Using the "Sniffer" Algorithm in the Electronic Medical Record. Journal of Nursing Care Quality 32(1): 25-31
Puranik, S.; Forno, E.; Bush, A.; Celedón, J.C. 2017: Predicting Severe Asthma Exacerbations in Children. American Journal of Respiratory and Critical Care Medicine 195(7): 854-859
Williams, D.J.; Zhu, Y.; Grijalva, C.G.; Self, W.H.; Harrell, F.E.; Reed, C.; Stockmann, C.; Arnold, S.R.; Ampofo, K.K.; Anderson, E.J.; Bramley, A.M.; Wunderink, R.G.; McCullers, J.A.; Pavia, A.T.; Jain, S.; Edwards, K.M. 2016: Predicting Severe Pneumonia Outcomes in Children. Pediatrics 138(4)
Tanigasalam, V.; Bhat, B.V.; Adhisivam, B.; Sridhar, M.G.; Harichandrakumar, K.T. 2016: Predicting Severity of Acute Kidney Injury in Term Neonates with Perinatal Asphyxia Using Urinary Neutrophil Gelatinase Associated Lipocalin. Indian Journal of Pediatrics 83(12-13): 1374-1378
Casey, E.A.; Masters, N.T.; Beadnell, B.; Hoppe, M.J.; Morrison, D.M.; Wells, E.A. 2017: Predicting Sexual Assault Perpetration Among Heterosexually Active Young Men. Violence Against Women 23(1): 3-27
Relyea, M.; Ullman, S.E. 2017: Predicting Sexual Assault Revictimization in a Longitudinal Sample of Women Survivors: Variation by Type of Assault. Violence Against Women 23(12): 1462-1483
Diehl, C.; Rees, J.; Bohner, G. 2018: Predicting Sexual Harassment from Hostile Sexism and Short-Term Mating Orientation: Relative Strength of Predictors Depends on Situational Priming of Power Versus Sex. Violence Against Women 24(2): 123-143
Howes, A.; Duggan, G.B.; Kalidindi, K.; Tseng, Y-Chi.; Lewis, R.L. 2016: Predicting Short-Term Remembering as Boundedly Optimal Strategy Choice. Cognitive Science 40(5): 1192-1223
Basques, B.A.; Chung, S.H.; Lukasiewicz, A.M.; Webb, M.L.; Samuel, A.M.; Bohl, D.D.; Smith, B.G.; Grauer, J.N. 2015: Predicting Short-term Morbidity in Patients Undergoing Posterior Spinal Fusion for Neuromuscular Scoliosis. Spine 40(24): 1910-1917
Diec, J.; Tilia, D.; Naduvilath, T.; Bakaraju, R.C. 2017: Predicting Short-term Performance of Multifocal Contact Lenses. Eye and Contact Lens 43(6): 340-345
Fichtenholtz, A.M.; Camarda, N.D.; Neumann, E.K. 2016: Predicting Significance of Unknown Variants in Glial Tumors through Sub-Class Enrichment. Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing 21: 297-308
Jiao, Y.; Zhou, L.; Ma, F.; Gao, G.; Kou, L.; Bell, J.; Sanvito, S.; Du, A. 2016: Predicting Single-Layer Technetium Dichalcogenides (TcX₂, X = S, Se) with Promising Applications in Photovoltaics and Photocatalysis. Acs Applied Materials and Interfaces 8(8): 5385-5392
Asghar, K.A.; Rowlands, D.A.; Elliott, J.M.; Squires, A.M. 2015: Predicting Sizes of Hexagonal and Gyroid Metal Nanostructures from Liquid Crystal Templating. Acs Nano 9(11): 10970-10978
Shaw, H.; Ellis, D.A.; Kendrick, L.-R.; Ziegler, F.; Wiseman, R. 2016: Predicting Smartphone Operating System from Personality and Individual Differences. Cyberpsychology Behavior and Social Networking 19(12): 727-732
Hoogendoorn, M.; Berger, T.; Schulz, A.; Stolz, T.; Szolovits, P. 2017: Predicting Social Anxiety Treatment Outcome Based on Therapeutic Email Conversations. IEEE Journal of Biomedical and Health Informatics 21(5): 1449-1459
Liu, Y.; Pan, X.; Wang, C.; Li, Y.; Shi, R. 2015: Predicting Soil Salinity with Vis-NIR Spectra after Removing the Effects of Soil Moisture Using External Parameter Orthogonalization. Plos one 10(10): E0140688
Hydren, J.R.; Foulis, S.A.; Frykman, P.N.; Warr, B.J.; Redmond, J.E.; Canino, M.C.; Cohen, B.S.; Sharp, M.A.; Zambraski, E.J. 2016: Predicting Soldier Load Carriage Performance: 960 Board #276 June 1, 2: 00 PM - 3: 30 PM. Medicine and Science in Sports and Exercise 48(5 Suppl 1): 274-274
Redmond, J.E.; Foulis, S.A.; Frykman, P.N.; Warr, B.J.; Sharp, M.A.; Zambraski, E.J. 2016: Predicting Soldier Performance of the Casualty Evacuation: 943 Board #259 June 1, 2: 00 PM - 3: 30 PM. Medicine and Science in Sports and Exercise 48(5 Suppl 1): 268-269
Zhang, X.; Li, J.; Wei, D.; Li, B.; Ma, Y. 2015: Predicting Soluble Nickel in Soils Using Soil Properties and Total Nickel. Plos one 10(7): E0133920
Misin, M.; Palmer, D.S.; Fedorov, M.V. 2016: Predicting Solvation Free Energies Using Parameter-Free Solvent Models. Journal of Physical Chemistry. B 120(25): 5724-5731
Osburn, C.L.; Handsel, L.T.; Peierls, B.L.; Paerl, H.W. 2016: Predicting Sources of Dissolved Organic Nitrogen to an Estuary from an Agro-Urban Coastal Watershed. Environmental Science and Technology 50(16): 8473-8484
Bellamy, C.; Altringham, J. 2015: Predicting Species Distributions Using Record Centre Data: Multi-Scale Modelling of Habitat Suitability for Bat Roosts. Plos one 10(6): E0128440
Rong, P.; Yunusova, Y.; Wang, J.; Zinman, L.; Pattee, G.L.; Berry, J.D.; Perry, B.; Green, J.R. 2016: Predicting Speech Intelligibility Decline in Amyotrophic Lateral Sclerosis Based on the Deterioration of Individual Speech Subsystems. Plos one 11(5): E0154971
Ellis, R.J.; Molander, P.; Rönnberg, J.; Lyxell, B.ör.; Andersson, G.; Lunner, T. 2016: Predicting Speech-in-Noise Recognition from Performance on the Trail Making Test: Results from a Large-Scale Internet Study. Ear and Hearing 37(1): 73-79
Dangle, P.; Ayyash, O.; Shaikh, H.; Stephany, H.A.; Cannon, G.M.; Schneck, F.X.; Ost, M.C. 2016: Predicting Spontaneous Stone Passage in Prepubertal Children: A Single Institution Cohort. Journal of Endourology 30(9): 945-949
Carpentier, Jëlle.; Mageau, Gève.A. 2016: Predicting Sport Experience During Training: The Role of Change-Oriented Feedback in Athletes' Motivation, Self-Confidence and Needs Satisfaction Fluctuations. Journal of Sport and Exercise Psychology 38(1): 45-58
Miller, N.A.; Kirk, A. 2016: Predicting State Investment in Medicaid Home- and Community-Based Services, 2000-2011. Journal of Aging and Social Policy 28(1): 49-64
Rasool, M.Fawad.; Khalil, F.; Läer, S. 2016: Predicting Stereoselective Disposition of Carvedilol in Adult and Pediatric Chronic Heart Failure Patients by Incorporating Pathophysiological Changes in Organ Blood Flows-A Physiologically Based Pharmacokinetic Approach. Drug Metabolism and Disposition: the Biological Fate of Chemicals 44(7): 1103-1115
Manuel, D.G.; Tuna, M.; Perez, R.; Tanuseputro, P.; Hennessy, D.; Bennett, C.; Rosella, L.; Sanmartin, C.; van Walraven, C.; Tu, J.V. 2015: Predicting Stroke Risk Based on Health Behaviours: Development of the Stroke Population Risk Tool (SPoRT). Plos one 10(12): E0143342
Safder, T.B.; Badgett, R.G. 2016: Predicting Stroke in Patients with Atrial Fibrillation: An Incomplete Picture Without Considering Quality of Anticoagulation. Journal of the American College of Cardiology 67(18): 2192-2193
Fredin, L.A.; Allison, T.C. 2016: Predicting Structures of Ru-Centered Dyes: a Computational Screening Tool. Journal of Physical Chemistry. a 120(13): 2135-2143
Wang, X.; Li, H.; Zhang, Q.; Wang, R. 2016: Predicting Subcellular Localization of Apoptosis Proteins Combining GO Features of Homologous Proteins and Distance Weighted KNN Classifier. Biomed Research International 2016: 1793272
Nielsen, H. 2017: Predicting Subcellular Localization of Proteins by Bioinformatic Algorithms. Current Topics in Microbiology and Immunology 404: 129-158
Schmack, K.; Burk, J.; Haynes, J.-D.; Sterzer, P. 2016: Predicting Subjective Affective Salience from Cortical Responses to Invisible Object Stimuli. Cerebral Cortex 26(8): 3453-3460
Valeri, L.; Patterson-Lomba, O.; Gurmu, Y.; Ablorh, A.; Bobb, J.; Townes, F.William.; Harling, G. 2016: Predicting Subnational Ebola Virus Disease Epidemic Dynamics from Sociodemographic Indicators. Plos one 11(10): E0163544
Sahebally, S.M. 2017: Predicting Suboptimal Bowel Preparation: Taking it up a PEG. Digestive Diseases and Sciences 62(2): 289-291
Lester, D. 2016: Predicting Success in Psychological Statistics Courses. Psychological Reports 118(3): 772-777
Hu, D. 2016: Predicting Success in Residency: the Quarterback Problem. Hospital Pharmacy 51(8): 615-617
Hollemans, R.A.; Bollen, T.L.; van Brunschot, S.; Bakker, O.J.; Ahmed Ali, U.; van Goor, H.; Boermeester, M.A.; Gooszen, H.G.; Besselink, M.G.; van Santvoort, H.C. 2016: Predicting Success of Catheter Drainage in Infected Necrotizing Pancreatitis. Annals of Surgery 263(4): 787-792
Al Fayyadh, M.J.; Heller, S.F.; Rajab, T.Konrad.; Gardner, A.K.; Bloom, J.P.; Rawlings, J.A.; Mullen, J.T.; Smink, D.S.; Farley, D.R.; Willis, R.E.; Dent, D.L. 2016: Predicting Success of Preliminary Surgical Residents: A Multi-Institutional Study. Journal of surgical education 73(6): e77-e83
Kawabata, K.; Watanabe, H.; Hara, K.; Bagarinao, E.; Yoneyama, N.; Ogura, A.; Imai, K.; Masuda, M.; Yokoi, T.; Ohdake, R.; Tanaka, Y.; Tsuboi, T.; Nakamura, T.; Hirayama, M.; Ito, M.; Atsuta, N.; Maesawa, S.; Naganawa, S.; Katsuno, M.; Sobue, G. 2018: Distinct manifestation of cognitive deficits associate with different resting-state network disruptions in non-demented patients with Parkinson's disease. Journal of Neurology 265(3): 688-700
Araújo, L.; Ribeiro, O.; Teixeira, L.; Paúl, Cça. 2016: Predicting Successful Aging at One Hundred Years of Age. Research on aging 38(6): 689-709
Barak-Corren, Y.; Castro, V.M.; Javitt, S.; Hoffnagle, A.G.; Dai, Y.; Perlis, R.H.; Nock, M.K.; Smoller, J.W.; Reis, B.Y. 2017: Predicting Suicidal Behavior From Longitudinal Electronic Health Records. American Journal of Psychiatry 174(2): 154-162
Sage, C.C.; Platko, J.V.; Nokela, M. 2014: Predicting Suicidal Behavior In Veterans And Active Military Personnel: Possibilities For Electronic Deployment To Discover A Predictive Assessment. Value in Health: the Journal of the International Society for Pharmacoeconomics and Outcomes Research 17(7): A569-A570
Jordan, J.T.; Samuelson, K.W. 2016: Predicting Suicide Intent: the Roles of Experiencing or Committing Violent Acts. Suicide and Life-Threatening Behavior 46(3): 293-300
Sarma, K.; Kumar, A.; Krishna, M.; Medhi, M.; Tripathi, O.P. 2015: Predicting Suitable Habitats for the Vulnerable Eastern Hoolock Gibbon, Hoolock leuconedys, in India Using the MaxEnt Model. Folia Primatologica; International Journal of Primatology 86(4): 387-397
Hutchings, F.; Han, C.E.; Keller, S.S.; Weber, B.; Taylor, P.N.; Kaiser, M. 2015: Predicting Surgery Targets in Temporal Lobe Epilepsy through Structural Connectome Based Simulations. Plos Computational Biology 11(12): E1004642
Barrera, J.é E.; Dion, G.R. 2016: Predicting Surgical Response Using Tensiometry in OSA Patients after Genioglossus Advancement with Uvulopalatopharyngoplasty. Otolaryngology--Head and Neck Surgery: Official Journal of American Academy of Otolaryngology-Head and Neck Surgery 154(3): 558-563
Rades, D.; Dziggel, L.; Hakim, S.G.; Rudat, V.; Janssen, S.; Trang, N.T.; Khoa, M.T.; Bartscht, T. 2015: Predicting Survival After Irradiation for Brain Metastases from Head and Neck Cancer. In Vivo 29(5): 525-528
Bolm, L.; Janssen, S.; Käsmann, L.; Wellner, U.; Bartscht, T.; Schild, S.E.; Rades, D. 2015: Predicting Survival After Irradiation of Metastases from Pancreatic Cancer. Anticancer Research 35(7): 4105-4108
Manig, L.; Käsmann, L.; Janssen, S.; Rades, D. 2016: Predicting Survival After Irradiation of Metastases from Transitional Carcinoma of the Bladder. Anticancer Research 36(12): 6663-6665
Glei, D.A.; Goldman, N.; Risques, R.Ana.; Rehkopf, D.H.; Dow, W.H.; Rosero-Bixby, L.; Weinstein, M. 2016: Predicting Survival from Telomere Length versus Conventional Predictors: A Multinational Population-Based Cohort Study. Plos one 11(4): E0152486
Karnik, N.D.; Gupta, A.V. 2016: Predicting Survival in ARDS. Journal of the Association of Physicians of India 63(11): 11-13
Graff-Radford, J.; Lesnick, T.G.; Boeve, B.F.; Przybelski, S.A.; Jones, D.T.; Senjem, M.L.; Gunter, J.L.; Ferman, T.J.; Knopman, D.S.; Murray, M.E.; Dickson, D.W.; Sarro, L.; Jack, C.R.; Petersen, R.C.; Kantarci, K. 2016: Predicting Survival in Dementia with Lewy Bodies with Hippocampal Volumetry. Movement Disorders: Official Journal of the Movement Disorder Society 31(7): 989-994
Albaeni, A.; Eid, S.M.; Vaidya, D.; Chandra-Strobos, N. 2014: Predicting Survival with Good Neurological Outcome Within 24 Hours Following Out of Hospital Cardiac Arrest:The Application and Validation of a Novel Clinical Score. Journal of Neurology and Translational Neuroscience 2(1)
Edwards, A.; Eisenberg, N.; Spinrad, T.L.; Reiser, M.; Eggum-Wilkens, N.D.; Liew, J. 2015: Predicting Sympathy and Prosocial Behavior from Young Children's Dispositional Sadness. Social Development 24(1): 76-94
Novak, D.; Riener, R. 2015: Predicting Targets of Human Reaching Motions with an Arm Rehabilitation Exoskeleton. Biomedical Sciences Instrumentation 51: 385-392
Hagen, G. 2015: Predicting The Future Economic Burden of Hip Fractures In Norway-The Impact of Epidemiological Uncertainty. Value in Health 18(7): A642
Grogan, J.A.; Markelc, B.; Connor, A.J.; Muschel, R.J.; Pitt-Francis, J.M.; Maini, P.K.; Byrne, H.M. 2017: Predicting the Influence of Microvascular Structure on Tumor Response to Radiotherapy. IEEE Transactions on Bio-Medical Engineering 64(3): 504-511
Young, A.S.; Meers, M.R.; Vesco, A.T.; Seidenfeld, A.M.; Arnold, L.Eugene.; Fristad, M.A. 2019: Predicting Therapeutic Effects of Psychodiagnostic Assessment Among Children and Adolescents Participating in Randomized Controlled Trials. Journal of Clinical Child and Adolescent Psychology: the Official Journal for the Society of Clinical Child and Adolescent Psychology American Psychological Association Division 53 48(Sup1): S1-S12
Atreya, R.; Neurath, M.F. 2016: Predicting Therapeutic Response by in vivo Molecular Imaging in Inflammatory Bowel Diseases. Digestive Diseases 34(5): 552-557
Kitadai, N. 2016: Predicting Thermodynamic Behaviors of Non-Protein Amino Acids as a Function of Temperature and pH. Origins of Life and Evolution of the Biosphere: the Journal of the International Society for the Study of the Origin of Life 46(1): 3-18
Xiao, F.; Peng, G.; Nie, C.; Yu, L. 2016: Predicting Thermodynamic Properties of PBXTHs with new Quantum Topological Indexes. Plos one 11(2): E0147126
Moreira, D.M.; Howard, L.E.; Sourbeer, K.N.; Amarasekara, H.S.; Chow, L.C.; Cockrell, D.C.; Pratson, C.L.; Hanyok, B.T.; Aronson, W.J.; Kane, C.J.; Terris, M.K.; Amling, C.L.; Cooperberg, M.R.; Freedland, S.J. 2017: Predicting time from Metastasis to Overall Survival in Castration-Resistant Prostate Cancer: Results from SEARCH. Clinical Genitourinary Cancer 15(1): 60-66.E2
Cooke, M.E.; Nasim, A.; Cho, S.Bin.; Kendler, K.S.; Clark, S.L.; Dick, D.M. 2016: Predicting Tobacco Use across the First Year of College. American journal of health behavior 40(4): 484-495
Basant, N.; Gupta, S.; Singh, K.P. 2015: Predicting Toxicities of Diverse Chemical Pesticides in Multiple Avian Species Using Tree-Based QSAR Approaches for Regulatory Purposes. Journal of Chemical Information and Modeling 55(7): 1337-1348
Klintström, E.; Klintström, B.; Moreno, R.; Brismar, T.B.; Pahr, D.H.; Smedby, Ör. 2016: Predicting Trabecular Bone Stiffness from Clinical Cone-Beam CT and HR-pQCT Data; an in Vitro Study Using Finite Element Analysis. Plos one 11(8): E0161101
Tauber, H.; Streif, W.; Fritz, J.; Ott, H.; Weigel, G.; Loacker, L.; Heinz, A.; Velik-Salchner, C. 2016: Predicting Transfusion Requirements During Extracorporeal Membrane Oxygenation. Journal of Cardiothoracic and Vascular Anesthesia 30(3): 692-701
Ruocco, A.C.; Rodrigo, A.H.; McMain, S.F.; Page-Gould, E.; Ayaz, H.; Links, P.S. 2016: Predicting Treatment Outcomes from Prefrontal Cortex Activation for Self-Harming Patients with Borderline Personality Disorder: a Preliminary Study. Frontiers in Human Neuroscience 10: 220
Andreescu, C.; Aizenstein, H. 2016: Predicting Treatment Response with Functional Magnetic Resonance Imaging. Biological Psychiatry 79(4): 262-263
Shenk, C.E.; Dorn, L.D.; Kolko, D.J.; Susman, E.J.; Noll, J.G.; Bukstein, O.G. 2012: Predicting Treatment Response for Oppositional Defiant and Conduct Disorder Using Pre-treatment Adrenal and Gonadal Hormones. Journal of Child and Family Studies 21(6): 973-981
Mattek, R.J.; Harris, S.E.; Fox, R.A. 2016: Predicting Treatment Success in Child and Parent Therapy Among Families in Poverty. Journal of Genetic Psychology 177(2): 44-54
Guitar, B.; Kazenski, D.; Howard, A.; Cousins, S.Freddie.; Fader, E.; Haskell, P. 2015: Predicting Treatment Time and Long-Term Outcome of the Lidcombe Program: A Replication and Reanalysis. American Journal of Speech-Language Pathology 24(3): 533-544
Tsai, M.-F.; Hwang, S.-L.; Tsay, S.-L.; Wang, C.-L.; Tsai, F.-C.; Chen, C.-C.; Huang, T.-Y. 2013: Predicting Trends in Dyspnea and Fatigue in Heart Failure Patients' Outcomes. Acta Cardiologica Sinica 29(6): 488-495
Harper, L.K.; Shoaf, A.L.; Bayse, C.A. 2015: Predicting Trigger Bonds in Explosive Materials through Wiberg Bond Index Analysis. Chemphyschem: a European Journal of Chemical Physics and Physical Chemistry 16(18): 3886-3892
Shi, Z.; Zhang, Z.; Kutana, A.; Yakobson, B.I. 2015: Predicting Two-Dimensional Silicon Carbide Monolayers. Acs Nano 9(10): 9802-9809
Vaala, S.E.; Hornik, R.C. 2014: Predicting US Infants' and Toddlers' TV/Video Viewing Rates: Mothers' Cognitions and Structural Life Circumstances. Journal of Children and Media 8(2): 163-182
Khanna, S.; Good, N.; Boyle, J. 2016: Predicting Unpanned Return to Hospital for Chronic Disease Patients. Studies in Health Technology and Informatics 227: 67-73
Rosenbaum, J.E.; Zenilman, J.; Rose, E.; Wingood, G.; DiClemente, R. 2016: Predicting Unprotected Sex and Unplanned Pregnancy among Urban African-American Adolescent Girls Using the Theory of Gender and Power. Journal of urban health: bulletin of the New York Academy of Medicine 93(3): 493-510
Baranowski, T.; Beltran, A.; Chen, T.-A.; Thompson, D.; O'Connor, T.; Hughes, S.; Diep, C.; Baranowski, J.C. 2013: Predicting use of Ineffective Responsive, Structure and Control Vegetable Parenting Practices with the Model of Goal Directed Behavior. Journal of Food Research 2(6): 80-88
Loe, H.; Nes, B.M.; Wisløff, U. 2016: Predicting VO2peak from Submaximal- and Peak Exercise Models: the HUNT 3 Fitness Study, Norway. Plos one 11(1): E0144873
Chen, N-Tsu.Nancy. 2015: Predicting Vaccination Intention and Benefit and Risk Perceptions: The Incorporation of Affect, Trust, and Television Influence in a Dual-Mode Model. Risk Analysis: An Official Publication of the Society for Risk Analysis 35(7): 1268-1280
Hohtari-Kivimäki, U.; Salminen, M.; Vahlberg, T.; Kivelä, S-Liisa. 2016: Predicting Value of Nine-Item Berg Balance Scale Among the Aged: A 3-Year Prospective Follow-Up Study. Experimental Aging Research 42(2): 151-160
Grieb, M.; Burkovski, A.; Sträng, J.E.; Kraus, J.M.; Groß, A.; Palm, G.ün.; Kühl, M.; Kestler, H.A. 2015: Predicting Variabilities in Cardiac Gene Expression with a Boolean Network Incorporating Uncertainty. Plos one 10(7): E0131832
Kalakoti, P.; Notarianni, C.; Nanda, A. 2016: Predicting Venous Thromboembolism in Pediatric Trauma Patients. JAMA Surgery 151(9): 881-882
Karaca, O.; Gunes, H.M.; Omaygenc, M.O.; Cakal, B.; Cakal, S.D.; Demir, G.G.; Kizilirmak, F.; Gokdeniz, T.; Barutcu, I.; Boztosun, B.; Kilicaslan, F. 2016: Predicting Ventricular Arrhythmias in Cardiac Resynchronization Therapy: the Impact of Persistent Electrical Dyssynchrony. Pacing and Clinical Electrophysiology: Pace 39(9): 969-977
Kim, Y.B.; Park, N.; Zhang, Q.; Kim, J.G.; Kang, S.J.; Kim, C.H. 2016: Predicting Virtual World User Population Fluctuations with Deep Learning. Plos one 11(12): E0167153
Skrivanova, K.; Anderkova, L.; Brancikova, D.; Jarkovský, J.; Benesova, K.; Elfmarková, N.; Svěrák, T.; Bendová, M.; Peterkova, H.; Nedvěd, J.; Protivánková, M.; Minar, L.; Holoubková, E.; Dusek, L. 2016: Predicting Vitality Change in Older Breast Cancer Survivors after Primary Treatment--an Approach Based on Using Time-related Difference of Pro-inflammatory Marker C-reactive Protein. Klinicka Onkologie: Casopis Ceske a Slovenske Onkologicke Spolecnosti 29(1): 52-58
Guo, H.; Wang, Z.; Zhang, Y. 2016: Predicting Vo2max From A 6-minute Stairs Climbing And Descending Test (6MSCDT): 355 Board #192 June 1, 11: 00 AM - 12: 30 PM. Medicine and Science in Sports and Exercise 48(5 Suppl 1): 93-93
Paulo, A.; Zaal, F.T.J.M.; Fonseca, S.; Araújo, D. 2016: Predicting Volleyball Serve-Reception. Frontiers in Psychology 7: 1694
Cook, P.G.; Miller, A.; Shanafield, M.; Simmons, C.T. 2017: Predicting Water Resource Impacts of Unconventional Gas Using Simple Analytical Equations. Ground Water 55(3): 387-398
Lau, S.L.; Muir, C.; Assur, Y.; Beach, R.; Tran, B.; Bartrop, R.; McLean, M.; Caetano, D. 2016: Predicting Weight Gain in Patients Treated with Clozapine: the Role of Sex, Body Mass Index, and Smoking. Journal of Clinical Psychopharmacology 36(2): 120-124
Shand, L.; Brown, W.M.; Chaves, L.F.; Goldberg, T.L.; Hamer, G.L.; Haramis, L.; Kitron, U.; Walker, E.D.; Ruiz, M.O. 2016: Predicting West Nile Virus Infection Risk from the Synergistic Effects of Rainfall and Temperature. Journal of Medical Entomology 53(4): 935-944
Warren, B.H.; Baudin, R.ém.; Franck, A.; Hugel, S.; Strasberg, D. 2016: Predicting Where a Radiation will Occur: Acoustic and Molecular Surveys Reveal Overlooked Diversity in Indian Ocean Island Crickets (Mogoplistinae: Ornebius). Plos one 11(2): E0148971
Hartjes, T.M. 2015: Predicting which Patients will Benefit from Palliative Care: use of Bundles, Triggers, and Protocols. Critical Care Nursing Clinics of North America 27(3): 307-314
Mareiniss, D.P.; Xu, T.; Pham, J.Cuong.; Hsieh, Y-Hsiang.; Zhao, J.; Nguyen, C.; Nguyen, M.; Winters, B. 2016: Predicting Which Patients will Likely Benefit from Subglottic Secretion Drainage Endotracheal Tubes: A Retrospective Study. Journal of Emergency Medicine 50(3): 385-393
Hancock, P.A.; White, V.L.; Ritchie, S.A.; Hoffmann, A.A.; Godfray, H.C.J. 2016: Predicting Wolbachia invasion dynamics in Aedes aegypti populations using models of density-dependent demographic traits. Bmc Biology 14(1): 96
Lambrechts, L. 2015: Predicting Wolbachia potential to knock down dengue virus transmission. Annals of Translational Medicine 3(19): 288
Mendoza, K.; Ulloa, A.; Saavedra, N.; Galván, J.; Berenzon, S. 2017: Predicting Women's Utilization of Primary Care Mental Health Services in Mexico City. Journal of Primary Care and Community Health 8(2): 83-88
Van Benschoten, A.H.; Afonine, P.V.; Terwilliger, T.C.; Wall, M.E.; Jackson, C.J.; Sauter, N.K.; Adams, P.D.; Urzhumtsev, A.; Fraser, J.S. 2015: Predicting X-ray diffuse scattering from translation-libration-screw structural ensembles. Acta Crystallographica. Section D Biological Crystallography 71(Pt 8): 1657-1667
Maggs, J.L.; Staff, J.; Kloska, D.D.; Patrick, M.E.; O'Malley, P.M.; Schulenberg, J. 2015: Predicting Young Adult Degree Attainment by Late Adolescent Marijuana Use. Journal of adolescent health: official publication of the Society for Adolescent Medicine 57(2): 205-211
Papp, L.M.; Kouros, C.D. 2017: Predicting young adults' risk for engaging in prescription drug misuse in daily life from individual, partner, and relationship factors. Substance Abuse 38(1): 61-68
Sichimba, F.; Mooya, H.; Mesman, J. 2017: Predicting Zambian Grandmothers' Sensitivity Toward their Grandchildren. International Journal of Aging and Human Development 85(2): 185-203
Cox, B.D.; Stanton, R.A.; Schinazi, R.F. 2015: Predicting Zika virus structural biology: Challenges and opportunities for intervention. Antiviral Chemistry and ChemoTherapy 24(3-4): 118-126
Guan, C.; Niu, X.; Shi, F.; Yang, K.; Li, N. 2015: Predicting a DNA-binding protein using random forest with multiple mathematical features. Bio-Medical Materials and Engineering 26(Suppl 1): S1883-S1889
Kasir, R.; Zakko, S.; Zakko, P.; Adler, M.; Lee, A.; Dhingra, S.; Guttermuth, C. 2016: Predicting a Response to Antibiotics in Patients with the Irritable Bowel Syndrome. Digestive Diseases and Sciences 61(3): 846-851
Roberts, A.M.I.; Tansey, C.; Smithers, R.J.; Phillimore, A.B. 2015: Predicting a change in the order of spring phenology in temperate forests. Global Change Biology 21(7): 2603-2611
Petersen, T.; Christensen, R.; Juhl, C. 2015: Predicting a clinically important outcome in patients with low back pain following McKenzie therapy or spinal manipulation: a stratified analysis in a randomized controlled trial. Bmc Musculoskeletal Disorders 16: 74
Scior, T.; Paiz-Candia, B.; Islas, Án.A.; Sánchez-Solano, A.; Millan-Perez Peña, L.; Mancilla-Simbro, C.; Salinas-Stefanon, E.M. 2015: Predicting a double mutant in the twilight zone of low homology modeling for the skeletal muscle voltage-gated sodium channel subunit beta-1 (Nav1.4 β1). Computational and Structural Biotechnology Journal 13: 229-240
Wang, P.S.; Ren, W.; Bellaiche, L.; Xiang, H.J. 2015: Predicting a ferrimagnetic phase of Zn(2)FeOsO(6) with strong magnetoelectric coupling. Physical Review Letters 114(14): 147204
Schwab, S.; Gebhardt, M.; Hessels, M.G.P.; Nusser, L. 2016: Predicting a high rate of self-assessed and parent-assessed peer problems--Is it typical for students with disabilities?. Research in Developmental Disabilities 49-50: 196-204
Ma, F.; Gao, G.; Jiao, Y.; Gu, Y.; Bilic, A.; Zhang, H.; Chen, Z.; Du, A. 2016: Predicting a new phase (T'') of two-dimensional transition metal di-chalcogenides and strain-controlled topological phase transition. Nanoscale 8(9): 4969-4975
Huerta, S. 2016: Predicting a pathological complete response in rectal cancer. Anti-Cancer Drugs 27(8): 709-710
Shamid, S.M.; Allender, D.W.; Selinger, J.V. 2014: Predicting a polar analog of chiral blue phases in liquid crystals. Physical Review Letters 113(23): 237801
Nipp, R.D.; Ryan, D.P. 2015: Predicting a response to FOLFIRINOX in pancreatic cancer. Journal of the National Cancer Institute 107(8)
Ljungquist, K.L.; Agnew, S.P.; Huang, J.I. 2015: Predicting a safe screw length for volar plate fixation of distal radius fractures: lunate depth as a marker for distal radius depth. Journal of Hand Surgery 40(5): 940-944
Lee, C.-H.; Lee, C.-C.; Hsieh, C.-C.; Hong, M.-Y.; Chi, C.-H. 2016: Predicting abscesses in adults with community-onset monomicrobial Enterobacteriaceae bacteremia: microorganisms matters. Diagnostic Microbiology and Infectious Disease 84(1): 74-79
Schouteten, R. 2017: Predicting absenteeism: screening for work ability or burnout. Occupational Medicine 67(1): 52-57
Adegbija, O.; Hoy, W.; Wang, Z. 2015: Predicting absolute risk of type 2 diabetes using age and waist circumference values in an aboriginal Australian community. Plos one 10(4): E0123788
Fort, H.; Mungan, M. 2015: Predicting abundances of plants and pollinators using a simple compartmental mutualistic model. PROCEEDINGS. Biological Sciences 282(1808): 20150592
Yost, M.J.; Gardner, J.; Bell, R.M.; Fann, S.A.; Lisk, J.R.; Cheadle, W.G.; Goldman, M.H.; Rawn, S.; Weigelt, J.A.; Termuhlen, P.M.; Woods, R.J.; Endean, E.D.; Kimbrough, J.; Hulme, M. 2015: Predicting academic performance in surgical training. Journal of Surgical Education 72(3): 491-499
Al-Ansari, A.A.; El Tantawi, M.M.A. 2015: Predicting academic performance of dental students using perception of educational environment. Journal of Dental Education 79(3): 337-344
Jensen, J.H. 2015: Predicting accurate absolute binding energies in aqueous solution: thermodynamic considerations for electronic structure methods. Physical Chemistry Chemical Physics: Pccp 17(19): 12441-12451
Levin, M.; Kaforou, M. 2016: Predicting active tuberculosis progression by RNA analysis. Lancet 387(10035): 2268-2270
Abu El-Atta, A.H.; Moussa, M.I.; Hassanien, A.E. 2015: Predicting activity approach based on new atoms similarity kernel function. Journal of Molecular Graphics and Modelling 60: 55-62
Como, F.; Carnesecchi, E.; Volani, S.; Dorne, J.L.; Richardson, J.; Bassan, A.; Pavan, M.; Benfenati, E. 2017: Predicting acute contact toxicity of pesticides in honeybees (Apis mellifera) through a k-nearest neighbor model. Chemosphere 166: 438-444
Honore, P.M.; Jacobs, R.; Hendrickx, I.; De Waele, E.; Van Gorp, V.; Spapen, H.D. 2015: Predicting acute kidney injury in severe trauma. a biomarker breakthrough?. Critical Care 19: 432
Sosnov, J.A.; Stewart, I.J.; Chung, K.K. 2015: Predicting acute kidney injury in severe trauma. A biomarker breakthrough? Authors' response. Critical Care 19: 432
Szerlip, H.M.; Chawla, L.S. 2016: Predicting acute kidney injury prognosis. Current Opinion in Nephrology and Hypertension 25(3): 226-231
Kumarasena, R.S.; Niriella, M.A.; Ranawaka, C.K.; Miththinda, J.K.N.D.; de Silva, A.P.; Dassanayaka, A.S.; de Silva, H.J. 2016: Predicting acute liver failure in dengue infection. Ceylon Medical Journal 61(1): 35-36
West, J.B. 2014: Predicting acute mountain sickness. High Altitude Medicine and Biology 15(4): 427
Sajadi, S.Fatemeh.; Hajjari, Z.; Zargar, Y.; Mehrabizade Honarmand, M.; Arshadi, N. 2014: Predicting addiction potential on the basis of early traumatic events, dissociative experiences, and suicide ideation. International Journal of High Risk Behaviors and Addiction 3(4): E20995
Ramsay, D.S.; Al-Noori, S.; Shao, J.; Leroux, B.G.; Woods, S.C.; Kaiyala, K.J. 2015: Predicting addictive vulnerability: individual differences in initial responding to a drug's pharmacological effects. Plos one 10(4): E0124740
Depeursinge, A.; Yanagawa, M.; Leung, A.N.; Rubin, D.L. 2015: Predicting adenocarcinoma recurrence using computational texture models of nodule components in lung CT. Medical Physics 42(4): 2054-2063
Paoli, J. 2015: Predicting adequate surgical margins for cutaneous squamous cell carcinoma with dermoscopy. British Journal of Dermatology 172(5): 1186-1187
Karanasiou, G.S.; Tripoliti, E.E.; Papadopoulos, T.G.; Kalatzis, F.G.; Goletsis, Y.; Naka, K.K.; Bechlioulis, A.; Errachid, A.; Fotiadis, D.I. 2016: Predicting adherence of patients with HF through machine learning techniques. Healthcare Technology Letters 3(3): 165-170
Vitalis, D. 2017: Predicting adherence to antiretroviral therapy among pregnant women in Guyana: Utility of the Health Belief Model. International Journal of Std and Aids 28(8): 756-765
Holmes, E.A.F.; Hughes, D.A.; Morrison, V.L. 2014: Predicting adherence to medications using health psychology theories: a systematic review of 20 years of empirical research. Value in health: the journal of the International Society for Pharmacoeconomics and Outcomes Research 17(8): 863-876
Evangelista, L.S.; Ghasemzadeh, H.; Lee, J.-A.; Fallahzadeh, R.; Sarrafzadeh, M.; Moser, D.K. 2017: Predicting adherence to use of remote health monitoring systems in a cohort of patients with chronic heart failure. Technology and Health Care: Official Journal of the European Society for Engineering and Medicine 25(3): 425-433
Franklin, J.M.; Krumme, A.A.; Shrank, W.H.; Matlin, O.S.; Brennan, T.A.; Choudhry, N.K. 2015: Predicting adherence trajectory using initial patterns of medication filling. American Journal of Managed Care 21(9): E537-E544
Cameron, A.; Ireland, A.J.; McKay, G.A.; Stark, A.; Lowe, D.J. 2017: Predicting admission at triage: are nurses better than a simple objective score?. Emergency Medicine Journal: Emj 34(1): 2-7
Luben, R.; Hayat, S.; Wareham, N.; Khaw, K.T. 2016: Predicting admissions and time spent in hospital over a decade in a population-based record linkage study: the EPIC-Norfolk cohort. Bmj Open 6(1): E009461
Hipwell, A.E.; Stepp, S.D.; Moses-Kolko, E.L.; Xiong, S.; Paul, E.; Merrick, N.; McClelland, S.; Verble, D.; Keenan, K. 2016: Predicting adolescent postpartum caregiving from trajectories of depression and anxiety prior to childbirth: a 5-year prospective study. Archives of Women's Mental Health 19(5): 871-882
Yu, C.; Li, X.; Zhang, W. 2015: Predicting adolescent problematic online game use from teacher autonomy support, basic psychological needs satisfaction, and school engagement: a 2-year longitudinal study. Cyberpsychology Behavior and Social Networking 18(4): 228-233
Barlett, C.P. 2015: Predicting adolescent's cyberbullying behavior: a longitudinal risk analysis. Journal of Adolescence 41: 86-95
Kertai, M.D.; Fontes, M.L. 2015: Predicting adrenal insufficiency in severe sepsis: the role of plasma-free cortisol. Critical Care Medicine 43(3): 715-716
Simmonds, M.; Llewellyn, A.; Owen, C.G.; Woolacott, N. 2016: Predicting adult obesity from childhood obesity: a systematic review and meta-analysis. Obesity Reviews: An Official Journal of the International Association for the Study of Obesity 17(2): 95-107
Ladabaum, U.; Patel, A.; Mannalithara, A.; Sundaram, V.; Mitani, A.; Desai, M. 2016: Predicting advanced neoplasia at colonoscopy in a diverse population with the National Cancer Institute colorectal cancer risk-assessment tool. Cancer 122(17): 2663-2670
de Gelder, J.; Lucke, J.A.; de Groot, B.; Fogteloo, A.J.; Anten, S.; Mesri, K.; Steyerberg, E.W.; Heringhaus, C.; Blauw, G.J.; Mooijaart, S.P. 2016: Predicting adverse health outcomes in older emergency department patients: the APOP study. Netherlands Journal of Medicine 74(8): 342-352
Van Hazebroek, B.C.M.; Olthof, T.; Goossens, F.A. 2017: Predicting aggression in adolescence: the interrelation between (a lack of) empathy and social goals. Aggressive Behavior 43(2): 204-214
Caiozzo, C.N.; Houston, J.; Grych, J. 2016: Predicting aggression in late adolescent romantic relationships: a short-term longitudinal study. Journal of Adolescence 53: 237-248
Banse, R.; Messer, M.; Fischer, I. 2015: Predicting aggressive behavior with the aggressiveness-IAT. Aggressive Behavior 41(1): 65-83
Furuhama, A.; Hasunuma, K.; Hayashi, T.I.; Tatarazako, N. 2016: Predicting algal growth inhibition toxicity: three-step strategy using structural and physicochemical properties. Sar and Qsar in Environmental Research 27(5): 343-362
LaMartina, J.; Jawa, A.; Stucken, C.; Merlin, G.; Tornetta, P. 2015: Predicting alignment after closed reduction and casting of distal radius fractures. Journal of Hand Surgery 40(5): 934-939
Zhang, W.B.; Pincus, Z. 2016: Predicting all-cause mortality from basic physiology in the Framingham Heart Study. Aging Cell 15(1): 39-48
Nguyen, O.K.; Makam, A.N.; Clark, C.; Zhang, S.; Xie, B.; Velasco, F.; Amarasingham, R.; Halm, E.A. 2016: Predicting all-cause readmissions using electronic health record data from the entire hospitalization: Model development and comparison. Journal of Hospital Medicine 11(7): 473-480
Wright, D.F.B.; Duffull, S.B.; Merriman, T.R.; Dalbeth, N.; Barclay, M.L.; Stamp, L.K. 2016: Predicting allopurinol response in patients with gout. British Journal of Clinical Pharmacology 81(2): 277-289
Sha, S.J.; Ghosh, P.M.; Lee, S.E.; Corbetta-Rastelli, C.; Jagust, W.J.; Kornak, J.; Rankin, K.P.; Grinberg, L.T.; Vinters, H.V.; Mendez, M.F.; Dickson, D.W.; Seeley, W.W.; Gorno-Tempini, M.; Kramer, J.; Miller, B.L.; Boxer, A.L.; Rabinovici, G.D. 2015: Predicting amyloid status in corticobasal syndrome using modified clinical criteria, magnetic resonance imaging and fluorodeoxyglucose positron emission tomography. Alzheimer's Research and Therapy 7(1): 8
Wang, J.; Ke, C.; Yu, Z.; Fu, L.; Dornseif, B. 2016: Predicting analysis time in events-driven clinical trials using accumulating time-to-event surrogate information. Pharmaceutical Statistics 15(3): 198-207
Chen, T.-T. 2016: Predicting analysis times in randomized clinical trials with cancer immunotherapy. Bmc Medical Research Methodology 16: 12
Phan, C.-B.; Koo, S. 2015: Predicting anatomical landmarks and bone morphology of the femur using local region matching. International Journal of Computer Assisted Radiology and Surgery 10(11): 1711-1719
Shpak, A.A.; Shkvorchenko, D.O.; Sharafetdinov, I.K.; Yukhanova, O.A. 2016: Predicting anatomical results of surgical treatment of idiopathic macular hole. International Journal of Ophthalmology 9(2): 253-257
Sabir, S.; Ahrar, K.; Matin, S.F. 2016: Predicting and Determining the Success of Percutaneous Ablation. Journal of Urology 196(1): 7-8
Zhu, L.; Zhu, L.; Wang, H.; Yan, J.; Liu, B.; Chen, W.; He, J.; Zhou, Z.; Yang, X. 2017: Predicting and Early Monitoring Treatment Efficiency of Cervical Cancer Under Concurrent Chemoradiotherapy by Intravoxel Incoherent Motion Magnetic Resonance Imaging. Journal of Computer Assisted Tomography 41(3): 422-429
Guo, Z.; Xiao, D.; Li, D.; Wang, X.; Wang, Y.; Yan, T.; Wang, Z. 2016: Predicting and Evaluating the Epidemic Trend of Ebola Virus Disease in the 2014-2015 Outbreak and the Effects of Intervention Measures. PloS one 11(4): e0152438
Reilly, J.P. 2016: Predicting and Eventually Preventing the Future: Sepsis Risk in Community-Dwelling Adults. Critical Care Medicine 44(7): 1425-1426
Fukuda, K.; Woodman, G.F. 2015: Predicting and Improving Recognition Memory Using Multiple Electrophysiological Signals in Real time. Psychological Science 26(7): 1026-1037
Juhász, C. 2016: Predicting and Preventing Epilepsy in Sturge-Weber Syndrome?. Pediatric Neurology Briefs 30(11): 43
Erickson, B.J.; Chalmers, P.N.; Bush-Joseph, C.A.; Romeo, A.A. 2016: Predicting and Preventing Injury in Major League Baseball. American Journal of Orthopedics 45(3): 152-156
Shi, C.; Yu, C.-H.; Zhang, W. 2016: Predicting and Screening Dielectric Transitions in a Series of Hybrid Organic-Inorganic Double Perovskites via an Extended Tolerance Factor Approach. Angewandte Chemie 55(19): 5798-5802
Chow, M.L.; Troussicot, L.; Martin, M.; Doumèche, B.; Guillière, F.; Lancelin, J-Marc. 2016: Predicting and Understanding the Enzymatic Inhibition of Human Peroxiredoxin 5 by 4-Substituted Pyrocatechols by Combining Funnel Metadynamics, Solution NMR, and Steady-State Kinetics. Biochemistry 55(24): 3469-3480
Mehmandar, M.; Soori, H.; Mehrabi, Y. 2016: Predicting and analyzing the trend of traffic accidents deaths in Iran in 2014 and 2015. International Journal of Critical Illness and Injury Science 6(2): 74-78
Michielsen, A.; Kalantari, Z.; Lyon, S.W.; Liljegren, E. 2016: Predicting and communicating flood risk of transport infrastructure based on watershed characteristics. Journal of Environmental Management 182: 505-518
Ratelle, J.T.; Kelm, D.J.; Halvorsen, A.J.; West, C.P.; Oxentenko, A.S. 2015: Predicting and communicating risk of clinical deterioration: an observational cohort study of internal medicine residents. Journal of general internal medicine 30(4): 448-453
Wang, D.-P.; Lai, J.-C.; Lai, H.-Y.; Mo, S.-R.; Zeng, K.-Y.; Li, C.-H.; Zuo, J.-L. 2018: Distinct Mechanical and Self-Healing Properties in two Polydimethylsiloxane Coordination Polymers with Fine-Tuned Bond Strength. Inorganic Chemistry 57(6): 3232-3242
Cai, H.; Lilburn, T.G.; Hong, C.; Gu, J.; Kuang, R.; Wang, Y. 2015: Predicting and exploring network components involved in pathogenesis in the malaria parasite via novel subnetwork alignments. Bmc Systems Biology 9(Suppl 4): S1
Lee-Dadswell, G.R. 2015: Predicting and identifying finite-size effects in current spectra of one-dimensional oscillator chains. Physical Review. e Statistical Nonlinear and Soft Matter Physics 91(1): 012138
Yue, C. 2015: Predicting and influencing training success: spatial abilities and instructional design. Medical Education 49(11): 1054-1055
Van Leer, E.; Connor, N.P. 2015: Predicting and influencing voice therapy adherence using social-cognitive factors and mobile video. American Journal of Speech-Language Pathology 24(2): 164-176
Mirzabeigi, M.N.; Wilson, A.J.; Fischer, J.P.; Basta, M.; Kanchwala, S.; Kovach, S.J.; Serletti, J.M.; Wu, L.C. 2015: Predicting and managing donor-site wound complications in abdominally based free flap breast reconstruction: improved outcomes with early reoperative closure. Plastic and Reconstructive Surgery 135(1): 14-23
Smith, A.N.; Christian, M.P.; Firebaugh, S.L.; Cooper, G.W.; Jamieson, B.G. 2015: Predicting and managing heat dissipation from a neural probe. Biomedical Microdevices 17(4): 81
Miller, A.; Mandeville, J. 2016: Predicting and measuring fluid responsiveness with echocardiography. Echo Research and Practice 3(2): G1-G12
Janowski, M.; Walczak, P.; Pearl, M.S. 2016: Predicting and optimizing the territory of blood-brain barrier opening by superselective intra-arterial cerebral infusion under dynamic susceptibility contrast MRi guidance. Journal of Cerebral Blood Flow and Metabolism: Official Journal of the International Society of Cerebral Blood Flow and Metabolism 36(3): 569-575
Linder, K.E.; Nicolau, D.P.; Nailor, M.D. 2016: Predicting and preventing antimicrobial resistance utilizing pharmacodynamics: Part I gram positive bacteria. Expert Opinion on Drug Metabolism and Toxicology 12(3): 267-280
Rippey, J.Cr.; Carr, P.J.; Cooke, M.; Higgins, N.; Rickard, C.M. 2016: Predicting and preventing peripheral intravenous cannula insertion failure in the emergency department: Clinician 'gestalt' wins again. Emergency Medicine Australasia: Ema 28(6): 658-665
Covert, K.L.; Fleming, J.N.; Staino, C.; Casale, J.P.; Boyle, K.M.; Pilch, N.A.; Meadows, H.B.; Mardis, C.R.; McGillicuddy, J.W.; Nadig, S.; Bratton, C.F.; Chavin, K.D.; Baliga, P.K.; Taber, D.J. 2016: Predicting and preventing readmissions in kidney transplant recipients. Clinical Transplantation 30(7): 779-786
Raza, K.; Klareskog, L.; Holers, V.Michael. 2016: Predicting and preventing the development of rheumatoid arthritis. Rheumatology 55(1): 1-3
Gizzi, M.; Oberic, L.; Massard, C.; Poterie, A.; Le Teuff, G.; Loriot, Y.; Albiges, L.; Baciarello, G.; Michels, J.; Bossi, A.; Blanchard, P.; Escudier, B.; Fizazi, K. 2016: Predicting and preventing thromboembolic events in patients receiving cisplatin-based chemotherapy for germ cell tumours. European Journal of Cancer 69: 151-157
Kim, M.; Chu, A.; Khan, Y.; Malik, S. 2015: Predicting and preventing vascular complications following percutaneous coronary intervention in women. Expert Review of Cardiovascular Therapy 13(2): 163-172
Kimchi, G.; Stlylianou, P.; Wohl, A.; Hadani, M.; Cohen, Z.R.; Zauberman, J.; Feldman, Z.; Spiegelmann, R.; Nissim, O.; Zivly, Z.; Penn, M.; Harnof, S. 2016: Predicting and reducing cranioplasty infections by clinical, radiographic and operative parameters - a historical cohort study. Journal of Clinical Neuroscience: Official Journal of the Neurosurgical Society of Australasia 34: 182-186
Turner, S. 2016: Predicting and reducing risk of exacerbations in children with asthma in the primary care setting: current perspectives. Pragmatic and Observational Research 7: 33-39
Mehta, V.K. 2016: Predicting and reducing risk of radiation pneumonitis in patients with non-small cell lung cancer: A literature review. Journal of Clinical Oncology 23(16_Suppl): 7344-7344
Deary, I.J.; Brett, C.E. 2015: Predicting and retrodicting intelligence between childhood and old age in the 6-Day Sample of the Scottish Mental Survey 1947. Intelligence 50: 1-9
Peñaranda, D.A.; Simonetti, J.A. 2015: Predicting and setting conservation priorities for Bolivian mammals based on biological correlates of the risk of decline. Conservation Biology: the Journal of the Society for Conservation Biology 29(3): 834-843
Becker, C.; Bouvier, E.; Ghestem, A.; Siyoucef, S.; Claverie, D.; Camus, F.ço.; Bartolomei, F.; Benoliel, J.-J.; Bernard, C. 2015: Predicting and treating stress-induced vulnerability to epilepsy and depression. Annals of Neurology 78(1): 128-136
Breed, G.A.; Golson, E.A.; Tinker, M.T. 2017: Predicting animal home-range structure and transitions using a multistate Ornstein-Uhlenbeck biased random walk. Ecology 98(1): 32-47
Progiou, A.G.; Ziomas, I.C. 2015: Predicting annual average particulate concentration in urban areas. Science of the Total Environment 532: 353-359
Peleg, M.; Kim, A.D.; Normand, M.D. 2015: Predicting anthocyanins' isothermal and non-isothermal degradation with the endpoints method. Food Chemistry 187: 537-544
Yang, X.; Liu, H.; Yang, Q.; Liu, J.; Chen, J.; Shi, L. 2016: Predicting anti-androgenic activity of bisphenols using molecular docking and quantitative structure-activity relationships. Chemosphere 163: 373-381
Gágyor, I.ó; Haasenritter, J.ör.; Bleidorn, J.; McIsaac, W.; Schmiemann, G.; Hummers-Pradier, E.; Himmel, W. 2016: Predicting antibiotic prescription after symptomatic treatment for urinary tract infection: development of a model using data from an RCT in general practice. British Journal of General Practice: the Journal of the Royal College of General Practitioners 66(645): E234-E240
Bianchi, R.; Schonfeld, I.S.; Laurent, E. 2015: Predicting antidepressant treatment without controlling for depression is ill-advised. Journal of Psychiatric Research 69: 180-181
Garcia-Domenech, R.; Zanni, R.; Galvez-Llompart, M.; Galvez, J. 2015: Predicting antiprotozoal activity of benzyl phenyl ether diamine derivatives through QSAR multi-target and molecular topology. Molecular Diversity 19(2): 357-366
Tseilikman, O.B.; Kozochkin, D.A.; Manukhina, E.B.; Downey, H.Fred.; Misharina, M.E.; Komelkova, M.V.; Nikitina, A.A.; Golodnii, S.V.; Dodohova, M.A.; Tseilikman, V.E. 2016: Predicting anxiety responses to halogenated glucocorticoid drugs using the hexobarbital sleep time test. Stress 19(4): 390-394
Yourganov, G.; Smith, K.G.; Fridriksson, J.; Rorden, C. 2015: Predicting aphasia type from brain damage measured with structural MRi. Cortex; a Journal Devoted to the Study of the Nervous System and Behavior 73: 203-215
Scafoglieri, A.; Clarys, J.P.; Bauer, J.ür.M.; Verlaan, S.; Van Malderen, L.; Vantieghem, S.; Cederholm, T.; Sieber, C.C.; Mets, T.; Bautmans, I. 2017: Predicting appendicular lean and fat mass with bioelectrical impedance analysis in older adults with physical function decline - the PROVIDE study. Clinical Nutrition 36(3): 869-875
Valle, A.; Pan, I.; Regueiro, B.; Suárez, N.; Tuero, E.án.; Nunes, A.R. 2015: Predicting approach to homework in Primary school students. Psicothema 27(4): 334-340
Basant, N.; Gupta, S.; Singh, K.P. 2015: Predicting aquatic toxicities of chemical pesticides in multiple test species using nonlinear QSTR modeling approaches. Chemosphere 139: 246-255
Templeman, M.A.; Kingsford, M.J. 2015: Predicting aqueous copper and zinc accumulation in the upside-down jellyfish Cassiopea maremetens through the use of biokinetic models. Environmental Monitoring and Assessment 187(7): 416
Ho, S.H.; Speldewinde, P.; Cook, A. 2017: Predicting arboviral disease emergence using Bayesian networks: a case study of dengue virus in Western Australia. Epidemiology and Infection 145(1): 54-66
Boulain, T.; Garot, D.; Vignon, P.; Lascarrou, J.-B.; Benzekri-Lefevre, D.; Dequin, P.-F. 2016: Predicting arterial blood gas and lactate from central venous blood analysis in critically ill patients: a multicentre, prospective, diagnostic accuracy study. British Journal of Anaesthesia 117(3): 341-349
Zheng, T.; Zhang, X.; Wang, Y.; Yu, X. 2016: Predicting associations between micro RNAs and target genes in breast cancer by bioinformatics analyses. Oncology Letters 12(2): 1067-1073
Luo, G.; Stone, B.L.; Fassl, B.; Maloney, C.G.; Gesteland, P.H.; Yerram, S.R.; Nkoy, F.L. 2015: Predicting asthma control deterioration in children. Bmc Medical Informatics and Decision Making 15: 84
Killane, I.; Sulaiman, I.; MacHale, E.; Breathnach, A.; Taylor, T.E.; Holmes, M.S.; Reilly, R.B.; Costello, R.W. 2016: Predicting asthma exacerbations employing remotely monitored adherence. Healthcare Technology Letters 3(1): 51-55
Konradsen, J.R.; Skantz, E.; Nordlund, B.ör.; Lidegran, M.; James, A.; Ono, J.; Ohta, S.; Izuhara, K.; Dahlén, S.-E.; Alving, K.; Hedlin, G. 2015: Predicting asthma morbidity in children using proposed markers of Th2-type inflammation. Pediatric Allergy and Immunology: Official Publication of the European Society of Pediatric Allergy and Immunology 26(8): 772-779
Sears, M.R. 2015: Predicting asthma outcomes. Journal of Allergy and Clinical Immunology 136(4): 829
Ram, S.; Zhang, W.; Williams, M.; Pengetnze, Y. 2015: Predicting asthma-related emergency department visits using big data. IEEE Journal of Biomedical and Health Informatics 19(4): 1216-1223
Ruiz, M.C.; Haapanen, S.; Tolvanen, A.; Robazza, C.; Duda, J.L. 2017: Predicting athletes' functional and dysfunctional emotions: The role of the motivational climate and motivation regulations. Journal of Sports Sciences 35(16): 1598-1606
Winkle, R.A.; Jarman, J.W.E.; Mead, R.Hardwin.; Engel, G.; Kong, M.H.; Fleming, W.; Patrawala, R.A. 2016: Predicting atrial fibrillation ablation outcome: The CAAP-AF score. Heart rhythm 13(11): 2119-2125
Freitas-Murrell, B.; Swift, J.K. 2015: Predicting attitudes toward seeking professional psychological help among Alaska Natives. American Indian and Alaska Native Mental Health Research 22(3): 21-35
Mendonça, C.; Escher, A.; van de Par, S.; Colonius, H. 2015: Predicting auditory space calibration from recent multisensory experience. Experimental Brain Research 233(7): 1983-1991
Eliakim-Raz, N.; Bates, D.W.; Leibovici, L. 2015: Predicting bacteraemia in validated models--a systematic review. Clinical Microbiology and Infection: the Official Publication of the European Society of Clinical Microbiology and Infectious Diseases 21(4): 295-301
José, R.J.; Brown, J.S. 2016: Predicting bacteraemia or rapid identification of the causative pathogen in community acquired pneumonia: where should the priority lie?. European Respiratory Journal 48(3): 619-622
Larsen, P.; Dai, Y.; Collart, F.R. 2015: Predicting bacterial community assemblages using an artificial neural network approach. Methods in Molecular Biology 1260: 33-43
Galarz, L.A.; Fonseca, G.G.; Prentice, C. 2016: Predicting bacterial growth in raw, salted, and cooked chicken breast fillets during storage. Food Science and Technology International 22(6): 461-474
Ding, H.; Liang, Z-Yong.; Guo, F-Biao.; Huang, J.; Chen, W.; Lin, H. 2016: Predicting bacteriophage proteins located in host cell with feature selection technique. Computers in Biology and Medicine 71: 156-161
Wikstrom, E.A.; McKeon, P.O. 2017: Predicting balance improvements following STARS treatments in chronic ankle instability participants. Journal of Science and Medicine in Sport 20(4): 356-361
Meyer, A.D.; Stevens, D.F.; Blackwood, J.C. 2016: Predicting bat colony survival under controls targeting multiple transmission routes of white-nose syndrome. Journal of Theoretical Biology 409: 60-69
Ye, Z.; Rae, C.L.; Nombela, C.; Ham, T.; Rittman, T.; Jones, P.S.; Rodríguez, P.V.áz.; Coyle-Gilchrist, I.; Regenthal, R.; Altena, E.; Housden, C.R.; Maxwell, H.; Sahakian, B.J.; Barker, R.A.; Robbins, T.W.; Rowe, J.B. 2016: Predicting beneficial effects of atomoxetine and citalopram on response inhibition in Parkinson's disease with clinical and neuroimaging measures. Human Brain Mapping 37(3): 1026-1037
Gireud-Goss, M.; Reyes, S.; Wilson, M.; Farley, M.; Memarzadeh, K.; Srinivasan, S.; Sirisaengtaksin, N.; Yamashita, S.; Tsunoda, S.; Lang, F.F.; Waxham, M.N.; Bean, A.J. 2018: Distinct mechanisms enable inward or outward budding from late endosomes/multivesicular bodies. Experimental Cell Research 372(1): 1-15
Regan, M.M. 2015: Predicting benefit of endocrine therapy for early breast cancer. Breast 24(Suppl 2): S129-S131
Horowitz-Kraus, T.; Eaton, K.; Farah, R.; Hajinazarian, A.; Vannest, J.; Holland, S.K. 2015: Predicting better performance on a college preparedness test from narrative comprehension at the age of 6 years: An fMRI study. Brain research 1629: 54-62
Klein, B.P.; Paffen, C.L.E.; Pas, S.F.T.; Dumoulin, S.O. 2016: Predicting bias in perceived position using attention field models. Journal of Vision 16(7): 15
Grainger, K.; Dodson, Z.; Korff, T. 2017: Predicting bicycle setup for children based on anthropometrics and comfort. Applied Ergonomics 59(Part A): 449-459
Yang, J.; Pak, Y.E.; Lee, T.-R. 2016: Predicting bifurcation angle effect on blood flow in the microvasculature. Microvascular Research 108: 22-28
Plauška, A.; Borst, J.G.; van der Heijden, M. 2016: Predicting binaural responses from monaural responses in the gerbil medial superior olive. Journal of Neurophysiology 115(6): 2950-2963
Chabot-Leclerc, A.; MacDonald, E.N.; Dau, T. 2016: Predicting binaural speech intelligibility using the signal-to-noise ratio in the envelope power spectrum domain. Journal of the Acoustical Society of America 140(1): 192
Basant, N.; Gupta, S.; Singh, K.P. 2016: Predicting binding affinities of diverse pharmaceutical chemicals to human serum plasma proteins using QSPR modelling approaches. Sar and Qsar in Environmental Research 27(1): 67-85
Grudinin, S.; Kadukova, M.; Eisenbarth, A.; Marillet, S.; Cazals, F.éd.ér. 2016: Predicting binding poses and affinities for protein - ligand complexes in the 2015 D3R Grand Challenge using a physical model with a statistical parameter estimation. Journal of Computer-Aided Molecular Design 30(9): 791-804
Fernandez, L.A.; Gschwend, P.M. 2015: Predicting bioaccumulation of polycyclic aromatic hydrocarbons in soft-shelled clams (Mya arenaria) using field deployments of polyethylene passive samplers. Environmental Toxicology and Chemistry 34(5): 993-1000
Anighoro, A.; de la Vega de León, A.; Bajorath, J.ür. 2016: Predicting bioactive conformations and binding modes of macrocycles. Journal of Computer-Aided Molecular Design 30(10): 841-849
Lin, S.; Wang, X.; Chao, Y.; He, Y.; Liu, M. 2016: Predicting biofilm thickness and biofilm viability based on the concentration of carbon-nitrogen-phosphorus by support vector regression. Environmental Science and Pollution Research International 23(1): 418-425
Muenster, U.; Mueck, W.; van der Mey, D.; Schlemmer, K.-H.; Greschat-Schade, S.; Haerter, M.; Pelzetter, C.; Pruemper, C.; Verlage, J.; Göller, A.H.; Ohm, A. 2016: Predicting biopharmaceutical performance of oral drug candidates - Extending the volume to dissolve applied dose concept. European Journal of Pharmaceutics and Biopharmaceutics: Official Journal of Arbeitsgemeinschaft für Pharmazeutische Verfahrenstechnik E.V 102: 191-201
Kadowaki, K.; Barbera, C.G.; Godsoe, W.; Delsuc, F.éd.ér.; Mouquet, N. 2016: Predicting biotic interactions and their variability in a changing environment. Biology Letters 12(5)
Cole, E.F.; Long, P.R.; Zelazowski, P.; Szulkin, M.; Sheldon, B.C. 2015: Predicting bird phenology from space: satellite-derived vegetation green-up signal uncovers spatial variation in phenological synchrony between birds and their environment. Ecology and Evolution 5(21): 5057-5074
Cox, P.G.; Rinderknecht, A.és.; Blanco, R.E. 2015: Predicting bite force and cranial biomechanics in the largest fossil rodent using finite element analysis. Journal of Anatomy 226(3): 215-223
Cook, M.; Smart, N.A.; Van der Touw, T. 2016: Predicting blood flow responses to rhythmic handgrip exercise from one second isometric contractions. Physiological Research 65(4): 581-589
Villa, L.; Sun, D.; Denhaerynck, K.; Vancayzeele, S.; Brié, H.; Hermans, C.; Aerts, A.; Levengood, M.; MacDonald, K.; Abraham, I. 2015: Predicting blood pressure outcomes using single-item physician-administered measures: a retrospective pooled analysis of observational studies in Belgium. British Journal of General Practice: the Journal of the Royal College of General Practitioners 65(630): E9-15
Schneider, C.; Boddy, A.P.; Fukuta, J.; Groom, W.D.; Streets, C.G. 2014: Predicting blood transfusion in patients undergoing minimally invasive oesophagectomy. International Journal of Surgery 12(12): 1342-1347
Shackelford, S.; Yang, S.; Hu, P.; Miller, C.; Anazodo, A.; Galvagno, S.; Wang, Y.; Hartsky, L.; Fang, R.; Mackenzie, C.; Anazodo, A.; Barker, S.; Blenko, J.; Chang, C.-I.; Chen, H.; Dinardo, T.; DuBose, J.; Fang, R.; Fouche, Y.; Goetz, L.; Grissom, T.; Giustina, V.; Hagegeorge, G.; Herrera, A.; Hess, J.; Hu, P.; Imle, C.; Mackenzie, C.; Menaker, J.; Murdock, K.; Narayan, M.; Oates, T.; Pasley, J.; Saccicchio, S.; Scalea, T.; Shackelford, S.; Sikorski, R.; Smith, L.; Stansbury, L.; Stein, D.; Stephens, C. 2015: Predicting blood transfusion using automated analysis of pulse oximetry signals and laboratory values. Journal of Trauma and Acute Care Surgery 79(4 Suppl 2: S175-S180
Andrew, R.; Tiggemann, M.; Clark, L. 2016: Predicting body appreciation in young women: An integrated model of positive body image. Body Image 18: 34-42
Wu, C.-S.; Chen, Y.-Y.; Chuang, C.-L.; Chiang, L.-M.; Dwyer, G.B.; Hsu, Y.-L.; Huang, A.-C.; Lai, C.-L.; Hsieh, K.-C. 2015: Predicting body composition using foot-to-foot bioelectrical impedance analysis in healthy Asian individuals. Nutrition Journal 14: 52
Kouda, K.; Ohara, K.; Nakamura, H.; Fujita, Y.; Iki, M. 2017: Predicting bone mineral acquisition during puberty: data from a 3-year follow-up study in Hamamatsu, Japan. Journal of Bone and Mineral Metabolism 35(2): 185-191
Marini, C.; Fossa, F.; Paoli, C.; Bellingeri, M.; Gnone, G.; Vassallo, P. 2015: Predicting bottlenose dolphin distribution along Liguria coast (northwestern Mediterranean Sea) through different modeling techniques and indirect predictors. Journal of Environmental Management 150: 9-20
Fernandino, L.; Humphries, C.J.; Seidenberg, M.S.; Gross, W.L.; Conant, L.L.; Binder, J.R. 2015: Predicting brain activation patterns associated with individual lexical concepts based on five sensory-motor attributes. Neuropsychologia 76: 17-26
Liem, F.; Varoquaux, G.ël.; Kynast, J.; Beyer, F.; Kharabian Masouleh, S.; Huntenburg, J.M.; Lampe, L.; Rahim, M.; Abraham, A.; Craddock, R.C.; Riedel-Heller, S.; Luck, T.; Loeffler, M.; Schroeter, M.L.; Witte, A.V.; Villringer, A.; Margulies, D.S. 2017: Predicting brain-age from multimodal imaging data captures cognitive impairment. Neuroimage 148: 179-188
Thomas, M.; De Brabanter, K.; Suykens, J.A.K.; De Moor, B. 2014: Predicting breast cancer using an expression values weighted clinical classifier. Bmc Bioinformatics 15: 411
Chowdhury, S.; Mazumder, A.J.; Husain, T. 2016: Predicting bromide incorporation in a chlorinated indoor swimming pool. Environmental Science and Pollution Research International 23(12): 12174-12184
Altman, R.B. 2015: Predicting cancer drug response: advancing the DREAM. Cancer Discovery 5(3): 237-238
Zhou, B.; Sun, Q.; Kong, D.-X. 2016: Predicting cancer-relevant proteins using an improved molecular similarity ensemble approach. Oncotarget 7(22): 32394-32407
Coburn, C. 2016: Predicting cancer: can we? Should we?. LANCET. Oncology 17(11): 1494
Wu, X.; Zhang, Q.; Wang, H.; Hu, J. 2015: Predicting carcinogenicity of organic compounds based on CPDB. Chemosphere 139: 81-90
Sato, A. 2015: Predicting cardiac and all-cause death in asymptomatic patients on hemodialysis – importance of training in interpretation of β-methyl iodophenyl-pentadecanoic acid single-photon emission computed tomography (BMIPP SPECT) imaging. Circulation Journal: Official Journal of the Japanese Circulation Society 79(1): 47-48
Yoshinaga, K. 2017: Predicting cardiac events using ventricular dyssynchrony in patients who received implantable cardioverter defibrillators: Are more treatment options required?. Journal of Nuclear Cardiology: Official Publication of the American Society of Nuclear Cardiology 24(1): 130-133
Jorge-Monjas, P.; Bustamante-Munguira, J.; Lorenzo, M.; Heredia-Rodríguez, M.ía.; Fierro, I.; Gómez-Sánchez, E.; Hernandez, A.; Álvarez, F.J.; Bermejo-Martin, J.ús.F.; Gómez-Pesquera, E.ía.; Gómez-Herreras, J.é I.; Tamayo, E. 2016: Predicting cardiac surgery-associated acute kidney injury: the CRATE score. Journal of Critical Care 31(1): 130-138
Markusse, I.M.; Meijs, J.; de Boer, B.; Bakker, J.A.; Schippers, H.Pascal.C.; Schouffoer, A.A.; Ajmone Marsan, N.; Kroft, L.J.M.; Ninaber, M.K.; Huizinga, T.W.J.; de Vries-Bouwstra, J.K. 2017: Predicting cardiopulmonary involvement in patients with systemic sclerosis: complementary value of nailfold videocapillaroscopy patterns and disease-specific autoantibodies. Rheumatology 56(7): 1081-1088
Pinsky, M.R.; Clermont, G.; Hravnak, M. 2016: Predicting cardiorespiratory instability. Critical Care 20: 70
Holt, T. 2016: Predicting cardiovascular disease. Bmj 353: I2621
Leong, D.P.; Teo, K.K. 2015: Predicting cardiovascular disease from handgrip strength: the potential clinical implications. Expert Review of Cardiovascular Therapy 13(12): 1277-1279
Brawner, C.A.; Ahmed, H.M. 2015: Predicting cardiovascular events … How FIT is our crystal ball?. Atherosclerosis 241(2): 741-742
Gore, M.Odette.; McGuire, D.K.; Lingvay, I.; Rosenstock, J. 2015: Predicting cardiovascular risk in type 2 diabetes: the heterogeneity challenges. Current Cardiology Reports 17(7): 607
Katsavos, S.; Artemiadis, A.K.; Zacharis, M.; Argyrou, P.; Theotoka, I.; Chrysovitsanou, C.; Anagnostouli, M. 2017: Predicting caregiving status and caregivers' burden in multiple sclerosis. A short report. Neurological Research 39(1): 13-15
Platteel, T.N.; Leverstein-van Hall, M.A.; Cohen Stuart, J.W.; Thijsen, S.F.T.; Mascini, E.M.; van Hees, B.C.; Scharringa, J.; Fluit, A.C.; Bonten, M.J.M. 2015: Predicting carriage with extended-spectrum beta-lactamase-producing bacteria at hospital admission: a cross-sectional study. Clinical Microbiology and Infection: the Official Publication of the European Society of Clinical Microbiology and Infectious Diseases 21(2): 141-146
Achiron, A.; Haddad, F.; Gerra, M.; Bartov, E.; Burgansky-Eliash, Z. 2016: Predicting cataract surgery time based on preoperative risk assessment. European Journal of Ophthalmology 26(3): 226-229
Weissmann, H.; Shnerb, N.M. 2016: Predicting catastrophic shifts. Journal of Theoretical Biology 397: 128-134
Kim, D.; Li, R.; Dudek, S.M.; Ritchie, M.D. 2015: Predicting censored survival data based on the interactions between meta-dimensional omics data in breast cancer. Journal of Biomedical Informatics 56: 220-228
Kikuchi, M.; Nakagawa, M.; Tone, S.; Saito, H.; Niino, T.; Nagasawa, N.; Sawai, J. 2016: Predicting changes in aquatic toxicity of chemicals resulting from solvent or dispersant use as vehicle. Chemosphere 154: 34-39
Kang, W.H.; Morrison, B. 2015: Predicting changes in cortical electrophysiological function after in vitro traumatic brain injury. Biomechanics and Modeling in Mechanobiology 14(5): 1033-1044
Masoud, M.I.; Marghalani, H.Y.A.; Bamashmous, M.; Alamoudi, N.M.; El Derwi, D.; Masoud, I.M.; Allareddy, V.; Gowharji, N.F. 2015: Predicting changes in mandibular length and total anterior facial height using IGF-1, cervical stage, skeletal classification, and gender. Progress in Orthodontics 16: 7
Tawara, D.; Nagura, K. 2017: Predicting changes in mechanical properties of trabecular bone by adaptive remodeling. Computer Methods in Biomechanics and Biomedical Engineering 20(4): 415-425
Ye, Z.J.; Qiu, H.Z.; Li, P.F.; Liang, M.Z.; Zhu, Y.F.; Zeng, Z.; Hu, G.Y.; Wang, S.N.; Quan, X.M. 2017: Predicting changes in quality of life and emotional distress in Chinese patients with lung, gastric, and colon-rectal cancer diagnoses: the role of psychological resilience. Psycho-Oncology 26(6): 829-835
Solomon, J.Wes.; Nielsen, R.D. 2015: Predicting changes in systolic blood pressure using longitudinal patient records. Journal of Biomedical Informatics 58 Suppl: S197-S202
Ehrlén, J.; Morris, W.F. 2015: Predicting changes in the distribution and abundance of species under environmental change. Ecology Letters 18(3): 303-314
Niehren, J.; Versari, C.; John, M.; Coutte, F.ço.; Jacques, P. 2016: Predicting changes of reaction networks with partial kinetic information. Bio Systems 149: 113-124
Hamilton, F.; Berry, T.; Sauer, T. 2015: Predicting chaotic time series with a partial model. Physical Review. e Statistical Nonlinear and Soft Matter Physics 92(1): 010902
Lee, B.; Kullman, S.W.; Yost, E.E.; Meyer, M.T.; Worley-Davis, L.; Williams, C.M.; Reckhow, K.H. 2015: Predicting characteristics of rainfall driven estrogen runoff and transport from swine AFO spray fields. Science of the Total Environment 532: 571-580
Gong, P.; Nan, X.; Barker, N.D.; Boyd, R.E.; Chen, Y.; Wilkins, D.E.; Johnson, D.R.; Suedel, B.C.; Perkins, E.J. 2016: Predicting chemical bioavailability using microarray gene expression data and regression modeling: a tale of three explosive compounds. Bmc Genomics 17: 205
Alves, V.M.; Muratov, E.; Fourches, D.; Strickland, J.; Kleinstreuer, N.; Andrade, C.H.; Tropsha, A. 2015: Predicting chemically-induced skin reactions. Part I: QSAR models of skin sensitization and their application to identify potentially hazardous compounds. Toxicology and Applied Pharmacology 284(2): 262-272
Alves, V.M.; Muratov, E.; Fourches, D.; Strickland, J.; Kleinstreuer, N.; Andrade, C.H.; Tropsha, A. 2015: Predicting chemically-induced skin reactions. Part II: QSAR models of skin permeability and the relationships between skin permeability and skin sensitization. Toxicology and Applied Pharmacology 284(2): 273-280
Savage, R.S.; Yuan, Y. 2016: Predicting chemoinsensitivity in breast cancer with 'omics/digital pathology data fusion. Royal Society Open Science 3(2): 140501
Meng, X.; Wang, G.; Guan, R.; Jia, X.; Gao, W.; Wu, J.; Yu, J.; Liu, P.; Yu, Y.; Sun, W.; Dong, H.; Fu, S. 2015: Predicting chemosensitivity to gemcitabine and cisplatin based on gene polymorphisms and mRNA expression in non-small-cell lung cancer cells. Pharmacogenomics 16(1): 23-34
Li, Y.; Sulik, M.J.; Eisenberg, N.; Spinrad, T.L.; Lemery-Chalfant, K.; Stover, D.A.; Verrelli, B.C. 2016: Predicting childhood effortful control from interactions between early parenting quality and children's dopamine transporter gene haplotypes. Development and Psychopathology 28(1): 199-212
Lourenço-Matharu, L.; Papineni McIntosh, A.; Lo, J.W. 2016: Predicting children's behaviour during dental treatment under oral sedation. European Archives of Paediatric Dentistry: Official Journal of the European Academy of Paediatric Dentistry 17(3): 157-163
Huang, J.; Marco, E.; Pinello, L.; Yuan, G.-C. 2015: Predicting chromatin organization using histone marks. Genome Biology 16: 162
Jacobsohn, D.A. 2016: Predicting chronic GVHD outcomes: are we there yet?. Blood 127(1): 14-15
Carrier, J-Daniel.; Roberge, P.; Courteau, J.; Vanasse, A. 2016: Predicting chronic benzodiazepine use in adults with depressive disorder: Retrospective cohort study using administrative data in Quebec. Canadian Family Physician Medecin de Famille Canadien 62(8): E473-E483
Taylor, N.S.; Kirwan, J.A.; Johnson, C.; Yan, N.D.; Viant, M.R.; Gunn, J.M.; McGeer, J.C. 2016: Predicting chronic copper and nickel reproductive toxicity to Daphnia pulex-pulicaria from whole-animal metabolic profiles. Environmental Pollution 212: 325-329
Panken, G.; Hoekstra, T.; Verhagen, A.; van Tulder, M.; Twisk, J.; Heymans, M.W. 2016: Predicting chronic low-back pain based on pain trajectories in patients in an occupational setting: an exploratory analysis. Scandinavian Journal of Work Environment and Health 42(6): 520-527
Basavanhally, A.; Viswanath, S.; Madabhushi, A. 2015: Predicting classifier performance with limited training data: applications to computer-aided diagnosis in breast and prostate cancer. Plos one 10(5): E0117900
Kelly, P.J.; Leung, J.; Deane, F.P.; Lyons, G.C.B. 2016: Predicting client attendance at further treatment following drug and alcohol detoxification: Theory of Planned Behaviour and Implementation Intentions. Drug and Alcohol Review 35(6): 678-685
Moor, H.; Hylander, K.; Norberg, J. 2015: Predicting climate change effects on wetland ecosystem services using species distribution modeling and plant functional traits. Ambio 44(Suppl 1): S113-S126
Hirsch, F.R.; Franklin, W.A.; McCoy, J.; Cappuzzo, F.; Varella-Garcia, M.; Witta, S.E.; Gumerlock, P.; West, H.; Gandara, D.R.; Bunn, P.A. 2016: Predicting clinical benefit from EGFR TKIs: Not all EGFR mutations are equal. Journal of Clinical Oncology 24(18_Suppl): 7072-7072
Hayward, G.N.; Vincent, C.; Lasserson, D.S. 2017: Predicting clinical deterioration after initial assessment in out-of-hours primary care: a retrospective service evaluation. British Journal of General Practice: the Journal of the Royal College of General Practitioners 67(654): E78-E85
Fenerty, K.E.; Folio, L.R.; Patronas, N.J.; Marté, J.L.; Gulley, J.L.; Heery, C.R. 2016: Predicting clinical outcomes in chordoma patients receiving immunotherapy: a comparison between volumetric segmentation and RECIST. Bmc Cancer 16(1): 672
Tong, M.J.; Huynh, T.T.; Siripongsakun, S.; Chang, P.W.; Tong, L.T.; Ha, Y.P.; Mena, E.A.; Weissman, M.F. 2015: Predicting clinical outcomes in patients with HBsAg-positive chronic hepatitis. Hepatology International 9(4): 567-577
Shahani, L.; Darouiche, R.O. 2016: Predicting clinical outcomes in patients with negative peripheral and positive central blood culture with coagulase negative Staphylococcus species. Hospital Practice 44(4): 179-182
Bailey, D.G. 2017: Predicting clinical relevance of grapefruit-drug interactions: a complicated process. Journal of Clinical Pharmacy and Therapeutics 42(2): 125-127
Mechelli, A.; Prata, D.; Kefford, C.; Kapur, S. 2015: Predicting clinical response in people at ultra-high risk of psychosis: a systematic and quantitative review. Drug Discovery Today 20(8): 924-927
Qin, J.; Shen, H.; Zeng, L.-L.; Jiang, W.; Liu, L.; Hu, D. 2015: Predicting clinical responses in major depression using intrinsic functional connectivity. Neuroreport 26(12): 675-680
Lee, H.K.; Tang, J.W.-T.; Loh, T.P.; Oon, L.L.-E.; Koay, E.S.-C. 2015: Predicting clinical severity based on substitutions near epitope a of influenza A/H3N2. Infection Genetics and Evolution: Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases 34: 292-297
Batty, R.A.; Francis, A.; Thomas, N.; Hopwood, M.; Ponsford, J.; Rossell, S.L. 2016: Predicting co-morbid traumatic brain injury and psychosis from neuropsychological profile. Schizophrenia Research 172(1-3): 143-144
Chappell, T.M.; Kennedy, G.G.; Walgenbach, J.F. 2015: Predicting codling moth (Cydia pomonella) phenology in North Carolina on the basis of temperature and improved generation turnover estimates. Pest Management Science 71(10): 1425-1432
Bolandzadeh, N.; Kording, K.; Salowitz, N.; Davis, J.C.; Hsu, L.; Chan, A.; Sharma, D.; Blohm, G.; Liu-Ambrose, T. 2015: Predicting cognitive function from clinical measures of physical function and health status in older adults. Plos one 10(3): E0119075
Foong, H.Foh.; Hamid, T.Aizan.; Ibrahim, R.; Haron, S.Azizah.; Shahar, S. 2018: Predicting cognitive function of the Malaysian elderly: a structural equation modelling approach. Aging and Mental Health 22(1): 109-120
Lopez, M.J.; Schuckers, M. 2017: Predicting coin flips: using resampling and hierarchical models to help untangle the NHL's shoot-out. Journal of Sports Sciences 35(9): 888-897
Wright, P.; Alex, A.; Pullen, F. 2016: Predicting collision-induced dissociation mass spectra: understanding the role of the mobile proton in small molecule fragmentation. Rapid Communications in Mass Spectrometry: Rcm 30(9): 1163-1175
Soguero-Ruiz, C.; Hindberg, K.; Mora-Jiménez, I.; Rojo-Álvarez, J.é L.; Skrøvseth, S.O.; Godtliebsen, F.; Mortensen, K.; Revhaug, A.; Lindsetmo, R.-O.; Augestad, K.M.; Jenssen, R. 2016: Predicting colorectal surgical complications using heterogeneous clinical data and kernel methods. Journal of Biomedical Informatics 61: 87-96
Cabral, R.G.; Chapman, C.E.; Aragona, K.M.; Clark, E.; Lunak, M.; Erickson, P.S. 2016: Predicting colostrum quality from performance in the previous lactation and environmental changes. Journal of Dairy Science 99(5): 4048-4055
Cadotte, M.W.; Arnillas, C.A.; Livingstone, S.W.; Yasui, S.-L.E. 2015: Predicting communities from functional traits. Trends in Ecology and Evolution 30(9): 510-511
Suttiwong, J.; Vongsirinavarat, M.; Chaiyawat, P.; Vachalathiti, R. 2015: Predicting community participation after spinal cord injury in Thailand. Journal of Rehabilitation Medicine 47(4): 325-329
Burbrink, F.T.; McKelvy, A.D.; Pyron, R.A.; Myers, E.A. 2015: Predicting community structure in snakes on Eastern Nearctic islands using ecological neutral theory and phylogenetic methods. PROCEEDINGS. Biological Sciences 282(1819)
Lord, J.; Whitlatch, R. 2015: Predicting competitive shifts and responses to climate change based on latitudinal distributions of species assemblages. Ecology 96(5): 1264-1274
Yang, T.J.; Goodman, K.A. 2015: Predicting complete response: is there a role for non-operative management of rectal cancer?. Journal of Gastrointestinal Oncology 6(2): 241-246
Mohn, C.E.; Kob, W. 2015: Predicting complex mineral structures using genetic algorithms. Journal of Physics. Condensed Matter: An Institute of Physics Journal 27(42): 425201
Kazanina, N. 2017: Predicting complex syntactic structure in real time: Processing of negative sentences in Russian. Quarterly Journal of Experimental Psychology 70(11): 2200-2218
Ramirez, D.; Maurice, M.J.; Caputo, P.A.; Nelson, R.J.; Kara, Ön.; Malkoç, E.; Kaouk, J.H. 2016: Predicting complications in partial nephrectomy for T1a tumours: does approach matter?. Bju International 118(6): 940-945
Tang, L.Y.; Hing, J.W.; Tang, J.Y.; Nishikawa, H.; Shahidullah, H.; Browne, F.; Chikermane, A.; Parulekar, M. 2016: Predicting complications with pretreatment testing in infantile haemangioma treated with oral propranolol. British Journal of Ophthalmology 100(7): 902-906
Kiri, L.; Boyd, D. 2015: Predicting composition-property relationships for glass ionomer cements: a multifactor central composite approach to material optimization. Journal of the Mechanical Behavior of Biomedical Materials 46: 285-291
Koch, S.; Benndorf, D.; Fronk, K.; Reichl, U.; Klamt, S. 2016: Predicting compositions of microbial communities from stoichiometric models with applications for the biogas process. Biotechnology for Biofuels 9: 17
Franquet-Griell, H.; Gómez-Canela, C.; Ventura, F.; Lacorte, S. 2015: Predicting concentrations of cytostatic drugs in sewage effluents and surface waters of Catalonia (NE Spain). Environmental Research 138: 161-172
Jiang, G.; Keller, J.; Bond, P.L.; Yuan, Z. 2016: Predicting concrete corrosion of sewers using artificial neural network. Water Research 92: 52-60
Espada, J.é P.; Morales, A.; Guillén-Riquelme, A.; Ballester, R.; Orgilés, M. 2016: Predicting condom use in adolescents: a test of three socio-cognitive models using a structural equation modeling approach. Bmc Public Health 16: 35
Platikanov, S.; Hernández, A.; González, S.; Luis Cortina, J.; Tauler, R.; Devesa, R. 2017: Predicting consumer preferences for mineral composition of bottled and tap water. Talanta 162: 1-9
Stilp, C.E.; Anderson, P.W.; Winn, M.B. 2015: Predicting contrast effects following reliable spectral properties in speech perception. Journal of the Acoustical Society of America 137(6): 3466-3476
Cabral, C.; Morgado, P.M.; Campos Costa, D.; Silveira, M. 2015: Predicting conversion from MCi to AD with FDG-PET brain images at different prodromal stages. Computers in Biology and Medicine 58: 101-109
Goonawardena, J.; Gunnarsson, R.; de Costa, A. 2015: Predicting conversion from laparoscopic to open cholecystectomy presented as a probability nomogram based on preoperative patient risk factors. American Journal of Surgery 210(3): 492-500
McGuinness, B.; Barrett, S.L.; McIlvenna, J.; Passmore, A.P.; Shorter, G.W. 2015: Predicting conversion to dementia in a memory clinic: a standard clinical approach compared with an empirically defined clustering method (latent profile analysis) for mild cognitive impairment subtyping. Alzheimer's and Dementia 1(4): 447-454
Kader, M.; Lamb, D.T.; Wang, L.; Megharaj, M.; Naidu, R. 2016: Predicting copper phytotoxicity based on pore-water pCu. Ecotoxicology 25(3): 481-490
Wang, W.-B.; Chen, S.; Wu, M.; Zhao, J. 2014: Predicting copper toxicity to Hypophthalmichthys molitrix and Ctenopharyngodon idellus based on biotic ligand model. Huan Jing Ke Xue= Huanjing Kexue 35(10): 3947-3951
Tristão, G.B.; Assunção, L.d.P.; Dos Santos, L.P.A.; Borges, C.L.; Silva-Bailão, M.G.; Soares, C.él.M.d.A.; Cavallaro, G.; Bailão, A.M. 2014: Predicting copper-, iron-, and zinc-binding proteins in pathogenic species of the Paracoccidioides genus. Frontiers in Microbiology 5: 761
Pereira, A.F.; Javaheri, B.; Pitsillides, A.A.; Shefelbine, S.J. 2015: Predicting cortical bone adaptation to axial loading in the mouse tibia. Journal of the Royal Society Interface 12(110): 0590
Cooper, E.A.; Norcia, A.M. 2015: Predicting cortical dark/bright asymmetries from natural image statistics and early visual transforms. Plos Computational Biology 11(5): E1004268
Boscardin, C.K.; Gonzales, R.; Bradley, K.L.; Raven, M.C. 2015: Predicting cost of care using self-reported health status data. Bmc Health Services Research 15: 406
Kwak, H.-C.; Kho, S. 2016: Predicting crash risk and identifying crash precursors on Korean expressways using loop detector data. Accident; Analysis and Prevention 88: 9-19
Scanlon, J.M.; Sherony, R.; Gabler, H.C. 2016: Predicting crash-relevant violations at stop sign-controlled intersections for the development of an intersection driver assistance system. Traffic Injury Prevention 17 Suppl. 1: 59-65
Braun, A.C.; Ilko, D.; Merget, B.; Gieseler, H.; Germershaus, O.; Holzgrabe, U.; Meinel, L. 2015: Predicting critical micelle concentration and micelle molecular weight of polysorbate 80 using compendial methods. European Journal of Pharmaceutics and Biopharmaceutics: Official Journal of Arbeitsgemeinschaft für Pharmazeutische Verfahrenstechnik E.V 94: 559-568
Weiss, V.C. 2015: Predicting critical temperatures of ionic and non-ionic fluids from thermophysical data obtained near the melting point. Journal of Chemical Physics 143(14): 144503
Zhang, Y. 2014: Predicting critical temperatures of iron(II) spin crossover materials: density functional theory plus U approach. Journal of Chemical Physics 141(21): 214703
Zurek, E.; Grochala, W. 2015: Predicting crystal structures and properties of matter under extreme conditions via quantum mechanics: the pressure is on. Physical Chemistry Chemical Physics: Pccp 17(5): 2917-2934
Chen, T.; Ma, Y.; Wang, Y. 2015: Predicting cumulative risk of disease onset by redistributing weights. Statistics in Medicine 34(16): 2427-2443
Barlett, C.; Chamberlin, K.; Witkower, Z. 2017: Predicting cyberbullying perpetration in emerging adults: a theoretical test of the Barlett Gentile Cyberbullying Model. Aggressive Behavior 43(2): 147-154
Diaz-Rodriguez, S.; Bozada, S.M.; Phifer, J.R.; Paluch, A.S. 2016: Predicting cyclohexane/water distribution coefficients for the SAMPL5 challenge using MOSCED and the SMD solvation model. Journal of Computer-Aided Molecular Design 30(11): 1007-1017
Jones, D.E.; Ghandehari, H.; Facelli, J.C. 2015: Predicting cytotoxicity of PAMAM dendrimers using molecular descriptors. Beilstein Journal of Nanotechnology 6: 1886-1896
Tian, D.; Zheng, W.; He, G.; Zheng, Y.; Andersen, M.E.; Tan, H.; Qu, W. 2015: Predicting cytotoxicity of complex mixtures in high cancer incidence regions of the Huai River Basin based on GC-MS spectrum with partial least squares regression. Environmental Research 137: 391-397
Wade, E.; Lin, P.; Hemmati, S.; Sigward, S. 2015: Predicting daily gait behaviors after anterior cruciate ligament surgery: a case study. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2015: 6752-6755
Rand, D.; Eng, J.J. 2015: Predicting daily use of the affected upper extremity 1 year after stroke. Journal of Stroke and Cerebrovascular Diseases: the Official Journal of National Stroke Association 24(2): 274-283
Tran, V.-T.; Porcher, R.; Tran, V.-C.; Ravaud, P. 2017: Predicting data saturation in qualitative surveys with mathematical models from ecological research. Journal of Clinical Epidemiology 82: 71-78.E2
Xie, Y.; Schreier, G.; Chang, D.C.W.; Neubauer, S.; Liu, Y.; Redmond, S.J.; Lovell, N.H. 2015: Predicting days in hospital using health insurance claims. IEEE Journal of Biomedical and Health Informatics 19(4): 1224-1233
O'Dowd, E.L.; Lüchtenborg, M.; Baldwin, D.R.; McKeever, T.M.; Powell, H.A.; Møller, H.; Jakobsen, E.; Hubbard, R.B. 2016: Predicting death from surgery for lung cancer: a comparison of two scoring systems in two European countries. Lung Cancer 95: 88-93
Brar, R.; Tangri, N. 2015: Predicting death without dialysis in elderly patients with CKD. Clinical Journal of the American Society of Nephrology: Cjasn 10(3): 341-343
Creutzfeldt, C.J.; Longstreth, W.T.; Holloway, R.G. 2015: Predicting decline and survival in severe acute brain injury: the fourth trajectory. Bmj 351: H3904
Abulaban, K.M.; Song, H.; Zhang, X.; Kimmel, P.L.; Kusek, J.W.; Nelson, R.G.; Feldman, H.I.; Vasan, R.S.; Ying, J.; Mauer, M.; Nelsestuen, G.L.; Bennett, M.; Brunner, H.I.; Rovin, B.H. 2016: Predicting decline of kidney function in lupus nephritis using urine biomarkers. Lupus 25(9): 1012-1018
Choi, J.; Dickson, P.; Calabrese, E.; Chen, S.; White, L.; Ellingwood, M.; Provenzale, J.M. 2015: Predicting degree of myelination based on diffusion tensor imagining of canines with mucopolysaccharidosis type i. Neuroradiology Journal 28(6): 562-573
Mourão, T.íl.B.; Mine, K.L.; Campos, E.F.; Medina-Pestana, J.O.; Tedesco-Silva, H.; Gerbase-DeLima, M. 2016: Predicting delayed kidney graft function with gene expression in preimplantation biopsies and first-day posttransplant blood. Human Immunology 77(4): 353-357
Takahashi, S.; Hoshino, M.; Takayama, K.; Iseki, K.; Sasaoka, R.; Tsujio, T.; Yasuda, H.; Sasaki, T.; Kanematsu, F.; Kono, H.; Toyoda, H.; Nakamura, H. 2016: Predicting delayed union in osteoporotic vertebral fractures with consecutive magnetic resonance imaging in the acute phase: a multicenter cohort study. Osteoporosis International: a Journal Established as Result of Cooperation Between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the Usa 27(12): 3567-3575
Fortes-Filho, S.Q.; Apolinario, D.; Melo, J.A.; Suzuki, I.; Sitta, M.d.C.; Garcez Leme, L.E. 2016: Predicting delirium after hip fracture with a 2-min cognitive screen: prospective cohort study. Age and Ageing 45(5): 713-717
Newman, M.W.; O'Dwyer, L.C.; Rosenthal, L. 2015: Predicting delirium: a review of risk-stratification models. General Hospital Psychiatry 37(5): 408-413
Siddiqi, N. 2016: Predicting delirium: time to use delirium risk scores in routine practice?. Age and Ageing 45(1): 9-10
Younge, K.C.; Roberts, D.; Janes, L.A.; Anderson, C.; Moran, J.M.; Matuszak, M.M. 2016: Predicting deliverability of volumetric-modulated arc therapy (VMAT) plans using aperture complexity analysis. Journal of Applied Clinical Medical Physics 17(4): 124-131
van Gool, W.A.; Richard, E. 2015: Predicting dementia. Bmj 350: H2994
Hessler, J.B.; Ander, K.-H.; Brönner, M.; Etgen, T.; Förstl, H.; Poppert, H.; Sander, D.; Bickel, H. 2016: Predicting dementia in primary care patients with a cardiovascular health metric: a prospective population-based study. Bmc Neurology 16: 116
Walters, K.; Hardoon, S.; Petersen, I.; Iliffe, S.; Omar, R.Z.; Nazareth, I.; Rait, G. 2016: Predicting dementia risk in primary care: development and validation of the Dementia Risk Score using routinely collected data. Bmc Medicine 14: 6
Halvari, A.E.Münster.; Halvari, H.; Williams, G.C.; Deci, E.L. 2017: Predicting dental attendance from dental hygienists' autonomy support and patients' autonomous motivation: A randomised clinical trial. Psychology and Health 32(2): 127-144
Kateeb, E.T.; McKernan, S.C.; Gaeth, G.J.; Kuthy, R.A.; Adrianse, N.B.; Damiano, P.C. 2016: Predicting dentists' decisions: a choice-based conjoint analysis of Medicaid participation. Journal of Public Health Dentistry 76(3): 171-178
Khandoker, A.H.; Luthra, V.; Abouallaban, Y.; Saha, S.; Ahmed, K.I.; Mostafa, R.; Chowdhury, N.; Jelinek, H.F. 2017: Predicting depressed patients with suicidal ideation from ECG recordings. Medical and Biological Engineering and Computing 55(5): 793-805
Angeli, S.I.; Goncalves, S. 2016: Predicting depth of electrode insertion by cochlear measurements on computed tomography scans. Laryngoscope 126(7): 1656-1661
Chen, B.; Yu, L.; Leng, S.; Kofler, J.; Favazza, C.; Vrieze, T.; McCollough, C. 2016: Predicting detection performance with model observers: Fourier domain or spatial domain?. Proceedings of Spie--the International Society for Optical Engineering 9783
Kittleson, M.D.; Stern, J.A.; Brown, D.J. 2015: Predicting development of subaortic stenosis in dogs. Journal of the American Veterinary Medical Association 246(10): 1058
Van't Hooft, J.; van der Lee, J.H.; Opmeer, B.C.; Aarnoudse-Moens, C.S.H.; Leenders, A.G.E.; Mol, B.W.J.; de Haan, T.R. 2015: Predicting developmental outcomes in premature infants by term equivalent MRI: systematic review and meta-analysis. Systematic Reviews 4: 71
Tang, X.; Hu, X.; Yang, X.; Fan, Y.; Li, Y.; Hu, W.; Liao, Y.; Zheng, M.C.; Peng, W.; Gao, L. 2016: Predicting diabetes mellitus genes via protein-protein interaction and protein subcellular localization information. Bmc Genomics 17(Suppl 4): 433
Yang, Y.; Zhang, S.; Lu, B.; Gong, W.; Dong, X.; Song, X.; Zhao, W.; Cui, J.; Liu, Y.; Hu, R. 2015: Predicting diabetic nephropathy by serum proteomic profiling in patients with type 2 diabetes. Wiener Klinische Wochenschrift 127(17-18): 669-674
Langer, S.L.; Seburg, E.; JaKa, M.M.; Sherwood, N.E.; Levy, R.L. 2017: Predicting dietary intake among children classified as overweight or at risk for overweight: Independent and interactive effects of parenting practices and styles. Appetite 110: 72-79
ElMaraachli, W.; Slater, M.; Berrada, Z.L.; Lin, S.-Y.G.; Catanzaro, A.; Desmond, E.; Rodrigues, C.; Victor, T.C.; Crudu, V.; Gler, M.T.; Rodwell, T.C. 2015: Predicting differential rifamycin resistance in clinical Mycobacterium tuberculosis isolates by specific rpoB mutations. International Journal of Tuberculosis and Lung Disease: the Official Journal of the International Union Against Tuberculosis and Lung Disease 19(10): 1222-1226
Yu, T.; Wang, B.; Jin, X.J.; Wu, R.R.; Wu, H.; He, J.J.; Yao, W.D.; Li, Y.H. 2015: Predicting difficult airways: 3-3-2 rule or 3-3 rule?. Irish Journal of Medical Science 184(3): 677-683
Pinto, J.; Cordeiro, L.; Pereira, C.; Gama, R.; Fernandes, H.L.; Assunção, J. 2016: Predicting difficult laryngoscopy using ultrasound measurement of distance from skin to epiglottis. Journal of Critical Care 33: 26-31
Xue, F.-S.; Sun, C.; Liu, G.-P.; Yang, G.-Z. 2016: Predicting difficult laryngoscopy using ultrasound technique. Journal of Critical Care 34: 131-132
Ohira, G.; Miyauchi, H.; Narushima, K.; Kagaya, A.; Mutou, Y.; Saitou, H.; Hayano, K.; Matsubara, H. 2017: Predicting difficulty in extending the ileal pouch to the anus in restorative proctocolectomy: investigation of a simple predictive method using computed tomography. Colorectal Disease: the Official Journal of the Association of Coloproctology of Great Britain and Ireland 19(1): O34-O38
Fang, X.; Vitrac, O. 2017: Predicting diffusion coefficients of chemicals in and through packaging materials. Critical Reviews in Food Science and Nutrition 57(2): 275-312
Soleimani Khashab, A.; Mansouri Khashab, A.; Mohammadi, M.R.; Zarabipour, H.; Malekpour, V. 2015: Predicting dimensions of psychological well being based on religious orientations and spirituality: an investigation into a causal model. Iranian Journal of Psychiatry 10(1): 50-55
Brinker, T.; Ellen, E.D.; Veerkamp, R.F.; Bijma, P. 2015: Predicting direct and indirect breeding values for survival time in laying hens using repeated measures. Genetics Selection Evolution: Gse 47: 75
Lassemo, E.; Sandanger, I.; Nygård, J.F.; Sørgaard, K.W. 2016: Predicting disability pension - depression as hazard: a 10 year population-based cohort study in Norway. International Journal of Methods in Psychiatric Research 25(1): 12-21
Mees, M.; Klein, J.; Yperzeele, L.; Vanacker, P.; Cras, P. 2016: Predicting discharge destination after stroke: a systematic review. Clinical Neurology and Neurosurgery 142: 15-21
Ouellette, D.S.; Timple, C.; Kaplan, S.E.; Rosenberg, S.S.; Rosario, E.R. 2015: Predicting discharge destination with admission outcome scores in stroke patients. Neurorehabilitation 37(2): 173-179
Cushman, L.A.; Poduri, K.R.; Palenski, C. 1995: Predicting discharge functional status and rehabilitation efficiency from preadmission functional assessments. Journal of Stroke and Cerebrovascular Diseases: the Official Journal of National Stroke Association 5(1): 33-38
Ho, K.C.; Speier, W.; El-Saden, S.; Liebeskind, D.S.; Saver, J.L.; Bui, A.A.T.; Arnold, C.W. 2014: Predicting discharge mortality after acute ischemic stroke using balanced data. AMIA . Annual Symposium Proceedings. AMIA Symposium 2014: 1787-1796
Masel, E.K.; Huber, P.; Schur, S.; Kierner, K.A.; Nemecek, R.; Watzke, H.H. 2014: Predicting discharge of palliative care inpatients by measuring their heart rate variability. Annals of Palliative Medicine 3(4): 244-249
Chou, S.; Khan, T.; Mahajan, H.; Pathmanathan, N. 2015: Predicting discordant HER2 results in ipsilateral synchronous invasive breast carcinomas: experience from a single institution. Pathology 47(7): 637-640
Taylor, A.A.; Fournier, C.; Polak, M.; Wang, L.; Zach, N.; Keymer, M.; Glass, J.D.; Ennist, D.L. 2016: Predicting disease progression in amyotrophic lateral sclerosis. Annals of Clinical and Translational Neurology 3(11): 866-875
Bang, J.; Lobach, I.V.; Lang, A.E.; Grossman, M.; Knopman, D.S.; Miller, B.L.; Schneider, L.S.; Doody, R.S.; Lees, A.; Gold, M.; Morimoto, B.H.; Boxer, A.L. 2016: Predicting disease progression in progressive supranuclear palsy in multicenter clinical trials. Parkinsonism and Related Disorders 28: 41-48
Yang, H.; Li, S.; Cao, H.; Zhang, C.; Cui, Y. 2017: Predicting disease trait with genomic data: a composite kernel approach. Briefings in Bioinformatics 18(4): 591-601
Smith, J.R.; Bagchi, R.; Ellens, J.; Kettle, C.J.; Burslem, D.F.R.P.; Maycock, C.R.; Khoo, E.; Ghazoul, J. 2015: Predicting dispersal of auto-gyrating fruit in tropical trees: a case study from the Dipterocarpaceae. Ecology and Evolution 5(9): 1794-1801
Scott, C.E.H.; Oliver, W.M.; MacDonald, D.; Wade, F.A.; Moran, M.; Breusch, S.J. 2016: Predicting dissatisfaction following total knee arthroplasty in patients under 55 years of age. Bone and Joint Journal 98-B(12): 1625-1634
Zhou, Z.; Folkert, M.; Cannon, N.; Iyengar, P.; Westover, K.; Zhang, Y.; Choy, H.; Timmerman, R.; Yan, J.; Xie, X.-J.; Jiang, S.; Wang, J. 2016: Predicting distant failure in early stage NSCLC treated with SBRT using clinical parameters. RadioTherapy and Oncology: Journal of the European Society for Therapeutic Radiology and Oncology 119(3): 501-504
Russell, B.; Collins, A.; Dowling, A.; Dally, M.; Gold, M.; Murphy, M.; Burchell, J.; Philip, J. 2016: Predicting distress among people who care for patients living longer with high-grade malignant glioma. Supportive Care in Cancer: Official Journal of the Multinational Association of Supportive Care in Cancer 24(1): 43-51
Gailani, J.Z.; Lackey, T.C.; King, D.B.; Bryant, D.; Kim, S.-C.; Shafer, D.J. 2016: Predicting dredging-associated effects to coral reefs in Apra Harbor, Guam - Part 1: Sediment exposure modeling. Journal of Environmental Management 168: 16-26
Nelson, D.S.; McManus, J.; Richmond, R.H.; King, D.B.; Gailani, J.Z.; Lackey, T.C.; Bryant, D. 2016: Predicting dredging-associated effects to coral reefs in Apra Harbor, Guam - Part 2: Potential coral effects. Journal of Environmental Management 168: 111-122
Van Diepen, J.B.; de Groot, I.W. 2016: Predicting drop-out during the systems training for emotional predictability and problem solving (STEPPS). Tijdschrift Voor Psychiatrie 58(4): 272-280
Batterham, M.; Tapsell, L.C.; Charlton, K.E. 2016: Predicting dropout in dietary weight loss trials using demographic and early weight change characteristics: Implications for trial design. Obesity Research and Clinical Practice 10(2): 189-196
Landes, S.J.; Chalker, S.A.; Comtois, K.A. 2016: Predicting dropout in outpatient dialectical behavior therapy with patients with borderline personality disorder receiving psychiatric disability. Borderline Personality Disorder and Emotion Dysregulation 3(1): 9
Voll, A.; Hernández-Ronquillo, L.; Buckley, S.; Téllez-Zenteno, J.F. 2015: Predicting drug resistance in adult patients with generalized epilepsy: a case-control study. Epilepsy and Behavior: E&b 53: 126-130
Zhang, W.; Liu, F.; Luo, L.; Zhang, J. 2015: Predicting drug side effects by multi-label learning and ensemble learning. Bmc Bioinformatics 16: 365
Fu, G.; Ding, Y.; Seal, A.; Chen, B.; Sun, Y.; Bolton, E. 2016: Predicting drug target interactions using meta-path-based semantic network analysis. Bmc Bioinformatics 17: 160
Shi, J.G.; Fraczkiewicz, G.; Williams, W.V.; Yeleswaram, S. 2015: Predicting drug-drug interactions involving multiple mechanisms using physiologically based pharmacokinetic modeling: a case study with ruxolitinib. Clinical Pharmacology and Therapeutics 97(2): 177-185
Roden, D.M. 2016: Predicting drug-induced QT prolongation and torsades de pointes. Journal of Physiology 594(9): 2459-2468
Zhang, H.; Ding, L.; Zou, Y.; Hu, S.-Q.; Huang, H.-G.; Kong, W.-B.; Zhang, J. 2016: Predicting drug-induced liver injury in human with Naïve Bayes classifier approach. Journal of Computer-Aided Molecular Design 30(10): 889-898
Shi, J.-Y.; Yiu, S.-M.; Li, Y.; Leung, H.C.M.; Chin, F.Y.L. 2015: Predicting drug-target interaction for new drugs using enhanced similarity measures and super-target clustering. Methods 83: 98-104
Kopylov, U.; Seidman, E. 2016: Predicting durable response or resistance to antitumor necrosis factor therapy in inflammatory bowel disease. Therapeutic Advances in Gastroenterology 9(4): 513-526
Carroll, J.M.; Solity, J.; Shapiro, L.R. 2016: Predicting dyslexia using prereading skills: the role of sensorimotor and cognitive abilities. Journal of Child Psychology and Psychiatry and Allied Disciplines 57(6): 750-758
Duchnowska, R.; Jassem, J.; Goswami, C.P.; Dundar, M.; Gökmen-Polar, Y.; Li, L.; Woditschka, S.; Biernat, W.; Sosińska-Mielcarek, K.; Czartoryska-Arłukowicz, B.ła.; Radecka, B.; Tomasevic, Z.; Stępniak, P.; Wojdan, K.; Sledge, G.W.; Steeg, P.S.; Badve, S. 2015: Predicting early brain metastases based on clinicopathological factors and gene expression analysis in advanced HER2-positive breast cancer patients. Journal of Neuro-Oncology 122(1): 205-216
Poitras, S.; Wood, K.S.; Savard, J.; Dervin, G.F.; Beaule, P.E. 2015: Predicting early clinical function after hip or knee arthroplasty. Bone and Joint Research 4(9): 145-151
Piñero, F.; Fauda, M.ín.; Quiros, R.; Mendizabal, M.; González-Campaña, A.; Czerwonko, D.; Barreiro, M.; Montal, S.; Silberman, E.; Coronel, M.ía.; Cacheiro, F.; Raffa, P.ía.; Andriani, O.; Silva, M.; Podestá, L.G. 2015: Predicting early discharge from hospital after liver transplantation (ERDALT) at a single center: a new model. Annals of Hepatology 14(6): 845-855
Narita, M.; Oussoultzoglou, E.; Chenard, M-Pierre.; Fuchshuber, P.; Yamamoto, T.; Addeo, P.; Jaeck, D.; Bachellier, P. 2015: Predicting early intrahepatic recurrence after curative resection of colorectal liver metastases with molecular markers. World Journal of Surgery 39(5): 1167-1176
Sohal, D.P.S.; Shrotriya, S.; Glass, K.T.; Pelley, R.J.; McNamara, M.J.; Estfan, B.; Shapiro, M.; Wey, J.; Chalikonda, S.; Morris-Stiff, G.; Walsh, R.M.; Khorana, A.A. 2015: Predicting early mortality in resectable pancreatic adenocarcinoma: a cohort study. Cancer 121(11): 1779-1784
Fei, X.; Lei, F.; Zhang, H.; Lu, H.; Zhu, Y.; Tang, Y. 2016: Predicting early post-chemotherapy adverse events in patients with hematological malignancies: a retrospective study. Supportive Care in Cancer: Official Journal of the Multinational Association of Supportive Care in Cancer 24(6): 2727-2733
Iwata, H.; Mori, E.; Tsuchiya, M.; Sakajo, A.; Maehara, K.; Ozawa, H.; Morita, A.; Maekawa, T.; Aoki, K.; Makaya, M.; Tamakoshi, K. 2015: Predicting early post-partum depressive symptoms among older primiparous Japanese mothers. Japan Journal of Nursing Science: Jjns 12(4): 297-308
Bellew, S.D.; Cabrera, D.; Lohse, C.M.; Bellolio, M.Fernanda. 2017: Predicting Early Rapid Response Team Activation in Patients Admitted From the Emergency Department: The PeRRT Score. Academic Emergency Medicine: Official Journal of the Society for Academic Emergency Medicine 24(2): 216-225
Kraft, I.; Schreiber, J.; Cafiero, R.; Metere, R.; Schaadt, G.; Brauer, J.; Neef, N.E.; Müller, B.; Kirsten, H.; Wilcke, A.; Boltze, J.; Friederici, A.D.; Skeide, M.A. 2016: Predicting early signs of dyslexia at a preliterate age by combining behavioral assessment with structural MRi. Neuroimage 143: 378-386
Muyan, M.; Chang, E.C.; Jilani, Z.; Yu, T. 2015: Predicting eating disturbances in Turkish adult females: Examining the role of intimate partner violence and perfectionism. Eating Behaviors 19: 102-105
Aumen, N.G.; Havens, K.E.; Best, G.R.; Berry, L. 2015: Predicting ecological responses of the Florida Everglades to possible future climate scenarios: introduction. Environmental Management 55(4): 741-748
Gilbert, J.A.; Henry, C. 2015: Predicting ecosystem emergent properties at multiple scales. Environmental Microbiology Reports 7(1): 20-22
Hon, K.L.; Tsang, Y.-C.K.; Poon, T.C.W.; Pong, N.H.; Kwan, M.; Lau, S.; Chiu, Y.-C.; Wong, H.-H.; Leung, T.-F. 2016: Predicting eczema severity beyond childhood. World Journal of Pediatrics: Wjp 12(1): 44-48
Agarwal, V.; Bell, G.W.; Nam, J.-W.; Bartel, D.P. 2015: Predicting effective microRNA target sites in mammalian mRNAs. Elife 4
Ma, L.; Xun, X.; Qiao, Y.; An, J.; Su, M. 2016: Predicting efficacies of anticancer drugs using single cell HaloChip assay. Analyst 141(8): 2454-2462
Peng, Q.; Rahul; Wang, G.; Liu, G.-R.; Grimme, S.; De, S. 2015: Predicting elastic properties of β-HMX from first-principles calculations. Journal of Physical Chemistry. B 119(18): 5896-5903
Fong, A.; Mittu, R.; Ratwani, R.; Reggia, J. 2014: Predicting electrocardiogram and arterial blood pressure waveforms with different Echo State Network architectures. AMIA .. Annual Symposium Proceedings. AMIA Symposium 2014: 544-553
Mordhorst, M.; Heidlauf, T.; Röhrle, O. 2015: Predicting electromyographic signals under realistic conditions using a multiscale chemo-electro-mechanical finite element model. Interface Focus 5(2): 20140076
Allen, J.; Taylor, J.; Dimeo, P.; Dixon, S.; Robinson, L. 2015: Predicting elite Scottish athletes' attitudes towards doping: examining the contribution of achievement goals and motivational climate. Journal of Sports Sciences 33(9): 899-906
Syed, Z.; Moscucci, M.; Share, D.; Gurm, H.S. 2015: Predicting emergency coronary artery bypass graft following PCI: application of a computational model to refer patients to hospitals with and without onsite surgical backup. Open Heart 2(1): E000243
Hsu, W-Chung.; Chen, L-Ching.; Wang, J-Yi. 2017: Predicting emerging care-need with simple functional indicators: Findings from a national cohort study in Taiwan. Geriatrics and Gerontology International 17(3): 375-381
Wilkey, J.; Kelly, K.; Jaramillo, I.Cristina.; Spinti, J.; Ring, T.; Hogue, M.; Pasqualini, D. 2016: Predicting emissions from oil and gas operations in the Uinta Basin, Utah. Journal of the Air and Waste Management Association 66(5): 528-545
Karimi, L.; Karimi, H.; Nouri, A. 2011: Predicting employees' well-being using work-family conflict and job strain models. Stress and Health: Journal of the International Society for the Investigation of Stress 27(2): 111-122
Bonomi, A.G.; Goldenberg, S.; Papini, G.; Kraal, J.; Stut, W.; Sartor, F.; Kemps, H. 2015: Predicting energy expenditure from photo-plethysmographic measurements of heart rate under beta blocker therapy: Data driven personalization strategies based on mixed models. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2015: 7642-7646
Farooqi, N.; Slinde, F.; Carlsson, M.; Håglin, L.; Sandström, T. 2015: Predicting energy requirement with pedometer-determined physical-activity level in women with chronic obstructive pulmonary disease. International Journal of Chronic Obstructive Pulmonary Disease 10: 1129-1137
Haller, M.; Myers, U.S.; McKnight, A.; Angkaw, A.C.; Norman, S.B. 2016: Predicting engagement in psychotherapy, pharmacotherapy, or both psychotherapy and pharmacotherapy among returning veterans seeking PTSD treatment. Psychological Services 13(4): 341-348
DeRolph, C.R.; Schramm, M.P.; Bevelhimer, M.S. 2016: Predicting environmental mitigation requirements for hydropower projects through the integration of biophysical and socio-political geographies. Science of the Total Environment 566-567: 888-918
Jager, T. 2016: Predicting environmental risk: A road map for the future. Journal of Toxicology and Environmental Health. Part a 79(13-15): 572-584
Valdano, E.; Poletto, C.; Giovannini, A.; Palma, D.; Savini, L.; Colizza, V. 2015: Predicting epidemic risk from past temporal contact data. Plos Computational Biology 11(3): E1004152
Ryvlin, P.; Rheims, S. 2016: Predicting epilepsy surgery outcome. Current Opinion in Neurology 29(2): 182-188
Russo, M.ía.J.; Cohen, G.; Chrem Mendez, P.; Campos, J.; Nahas, F.E.; Surace, E.I.; Vazquez, S.; Gustafson, D.; Guinjoan, S.; Allegri, R.F.; Sevlever, G. 2016: Predicting episodic memory performance using different biomarkers: results from Argentina-Alzheimer's Disease Neuroimaging Initiative. Neuropsychiatric Disease and Treatment 12: 2199-2206
Parinet, J.; Julien, M.; Nun, P.; Robins, R.J.; Remaud, G.; Höhener, P. 2015: Predicting equilibrium vapour pressure isotope effects by using artificial neural networks or multi-linear regression - A quantitative structure property relationship approach. Chemosphere 134: 521-527
Bode, S.; Stahl, J. 2014: Predicting errors from patterns of event-related potentials preceding an overt response. Biological Psychology 103: 357-369
Zhang, F.; Wu, M.; Li, X.-J.; Li, X.-L.; Kwoh, C.K.; Zheng, J. 2015: Predicting essential genes and synthetic lethality via influence propagation in signaling pathways of cancer cell fates. Journal of Bioinformatics and Computational Biology 13(3): 1541002
Li, G.; Li, M.; Wang, J.; Wu, J.; Wu, F-Xiang.; Pan, Y. 2016: Predicting essential proteins based on subcellular localization, orthology and PPI networks. Bmc Bioinformatics 17 Suppl. 8: 279
Yapuncich, G.S.; Gladman, J.T.; Boyer, D.M. 2015: Predicting euarchontan body mass: a comparison of tarsal and dental variables. American Journal of Physical Anthropology 157(3): 472-506
Ashander, J.; Chevin, L.-M.; Baskett, M.L. 2016: Predicting evolutionary rescue via evolving plasticity in stochastic environments. PROCEEDINGS. Biological Sciences 283(1839)
Nagamatsu, Y.; Sueyoshi, S.; Sasahara, H.; Oka, Y.; Kumazoe, H.; Mitsuoka, M.; Akagi, Y. 2016: Predicting exercise capacity after lobectomy by single photon emission computed tomography and computed tomography. General Thoracic and Cardiovascular Surgery 64(9): 537-542
Shi, J.-Y.; Li, J.-X.; Lu, H.-M. 2016: Predicting existing targets for new drugs base on strategies for missing interactions. Bmc Bioinformatics 17(Suppl 8): 282
Ewels, C.P.; Rocquefelte, X.; Kroto, H.W.; Rayson, M.J.; Briddon, P.R.; Heggie, M.I. 2015: Predicting experimentally stable allotropes: Instability of penta-graphene. Proceedings of the National Academy of Sciences of the United States of America 112(51): 15609-15612
Stark, K.; Andersson, P.; Beresford, N.A.; Yankovich, T.L.; Wood, M.D.; Johansen, M.P.; Vives i Batlle, J.; Twining, J.; Keum, D.-K.; Bollhöfer, A.; Doering, C.; Ryan, B.; Grzechnik, M.; Vandenhove, H. 2015: Predicting exposure of wildlife in radionuclide contaminated wetland ecosystems. Environmental Pollution 196: 201-213
Madaniyazi, L.; Guo, Y.; Chen, R.; Kan, H.; Tong, S. 2016: Predicting exposure-response associations of ambient particulate matter with mortality in 73 Chinese cities. Environmental Pollution 208(Pt A): 40-47
Budden, D.M.; Hurley, D.G.; Cursons, J.; Markham, J.F.; Davis, M.J.; Crampin, E.J. 2014: Predicting expression: the complementary power of histone modification and transcription factor binding data. Epigenetics and Chromatin 7(1): 36
Ma, Z.; Zhang, H.; Chien, S.I.-J.; Wang, J.; Dong, C. 2017: Predicting expressway crash frequency using a random effect negative binomial model: a case study in China. Accident; Analysis and Prevention 98: 214-222
Kitzes, J.; Harte, J. 2015: Predicting extinction debt from community patterns. Ecology 96(8): 2127-2136
Su, Z.; Duan, Z.; Pan, W.; Wu, C.; Jia, Y.; Han, B.; Li, C. 2016: Predicting extracapsular spread of head and neck cancers using different imaging techniques: a systematic review and meta-analysis. International Journal of Oral and Maxillofacial Surgery 45(4): 413-421
Cant, J.P.; Madsen, T.G.; Cieslar, S.R.L. 2016: Predicting extraction and uptake of arterial energy metabolites by the mammary glands of lactating cows when blood flow is perturbed. Journal of Dairy Science 99(1): 718-732
Aarnoudse-Moens, C.S.H. 2015: Predicting extremely preterm children's school performance by transient abnormal neurology?. Developmental Medicine and Child Neurology 57(9): 794-795
Liang, M.; Hu, X. 2015: Predicting eye fixations with higher-level visual features. IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society 24(3): 1178-1189
Fagertun, J.; Wolffhechel, K.; Pers, T.H.; Nielsen, H.B.; Gudbjartsson, D.; Stefansson, H.; Stefansson, K.ár.; Paulsen, R.R.; Jarmer, H. 2015: Predicting facial characteristics from complex polygenic variations. Forensic Science International. Genetics 19: 263-268
Li, B-Lin.; Li, W.; Bi, J-Qi.; Meng, Q-Gang.; Fei, J-Feng. 2016: Predicting factors associated with frailty in aged patients with bone-arthrosis pain in the clinic. Physician and Sportsmedicine 44(4): 391-396
Ranjbar, M.; Shoghli, A.; Kolifarhood, G.; Tabatabaei, S.Mehdi.; Amlashi, M.; Mohammadi, M. 2016: Predicting factors for malaria re-introduction: an applied model in an elimination setting to prevent malaria outbreaks. Malaria Journal 15: 138
Ferrer, A.; Formiga, F.; Cunillera, O.; Megido, M.J.ús.; Corbella, X.; Almeda, J.ús.; Almeda, J.; Cunillera, O.; Fernández, C.; Gil, A.; Llopart, C.; Megido, M.J.; Padrós, G.; Sarró, M.; Tobella, A. 2015: Predicting factors of health-related quality of life in octogenarians: a 3-year follow-up longitudinal study. Quality of Life Research: An International Journal of Quality of Life Aspects of Treatment Care and Rehabilitation 24(11): 2701-2711
Bordier, L.; Buysschaert, M.; Bauduceau, B.; Doucet, J.; Verny, C.; Lassmann Vague, V.; Le Floch, J.P.; Bauduceau, B.; Blicklé, J.-F.; Bourdel-Marchasson, I.; Constans, T.; Doucet, J.; Fagot-Campagna, A.; Kaloustian, E.; Lassmann-Vague, V.; Lecomte, P.; Tessier, D.; Verny, C.; Vischer, U.; Affres, H.; Alix, M.; Archambeaud, F.; Barrou, Z.; Bauduceau, B.; Beau, P.; Beltran, S.; Benoit, C.; Beressi, J.-P.; Bernachon, F.; Berne, C.; Berrut, G.; Blaimont, A.; Blickle, J.-F.; Boda-Buccino, M.; Bohatier, J.; Böhme, P.; Bordier, L.; Bouchou, K.; Bouillet, B.; Bouilloud, F.; Bouix, R.; Boulanger, E.; Bourdel-Marchasson, I.; Bourgon, C.; Bourrinet, E.; Brocker, P.; Bruckert, I.; Capet, C.; Carette, C.; Cariou, B.; Carreau, A.; Vaurie, C.C.; Chamou 2015: Predicting factors of hypoglycaemia in elderly type 2 diabetes patients: Contributions of the GERODIAB study. Diabetes and Metabolism 41(4): 301-303
Mirbolouk, F.; Yousefnezhad, A.; Ghanbari, A. 2015: Predicting factors of medical treatment success with single dose methotrexate in tubal ectopic pregnancy: a retrospective study. Iranian Journal of Reproductive Medicine 13(6): 351-354
Ten Hoeve, Y.; Castelein, S.; Jansen, W.; Jansen, G.; Roodbol, P. 2016: Predicting factors of positive orientation and attitudes towards nursing: A quantitative cross-sectional study. Nurse Education Today 40: 111-117
Chaturvedi, S.; Campbell, B. 2016: Predicting failure of acute stroke intervention. Neurology 87(15): 1528-1529
Sano, H.; Imagawa, K.; Yamamoto, N.; Ozawa, H.; Yokobori, A.T.; Itoi, E. 2015: Predicting failures of suture anchors used for rotator cuff repair: a CT-based 3-dimensional finite element analysis. Bio-Medical Materials and Engineering 25(4): 371-380
Anonymous 2001: Predicting fallers in a communitybased sample of people with Parkinson's disease. Nursing Older People 13(8): 8
Lazkani, A.; Delespierre, T.; Bauduceau, B.; Benattar-Zibi, L.; Bertin, P.; Berrut, G.; Corruble, E.; Danchin, N.; Derumeaux, G.èv.; Doucet, J.; Falissard, B.; Forette, F.; Hanon, O.; Pasquier, F.; Pinget, M.; Ourabah, R.; Piedvache, C.; Becquemont, L. 2015: Predicting falls in elderly patients with chronic pain and other chronic conditions. Aging Clinical and Experimental Research 27(5): 653-661
Cardon-Verbecq, C.; Loustau, M.; Guitard, E.; Bonduelle, M.; Delahaye, E.; Koskas, P.; Raynaud-Simon, A. 2017: Predicting falls with the cognitive timed up-and-go dual task in frail older patients. Annals of Physical and Rehabilitation Medicine 60(2): 83-86
McKechnie, D.; Pryor, J.; Fisher, M.J. 2016: Predicting falls: considerations for screening tool selection vs. screening tool development. Journal of Advanced Nursing 72(9): 2238-2250
Menichini, C.; Cheng, Z.; Gibbs, R.G.J.; Xu, X.Yun. 2016: Predicting false lumen thrombosis in patient-specific models of aortic dissection. Journal of the Royal Society Interface 13(124)
Carabús, A.; Sainz, R.D.; Oltjen, J.W.; Gispert, M.; Font-i-Furnols, M. 2015: Predicting fat, lean and the weights of primal cuts for growing pigs of different genotypes and sexes using computed tomography. Journal of Animal Science 93(3): 1388-1397
Denkinger, M.D.; Hasch, M.; Gerstmayer, A.; Kreienberg, R.; Nikolaus, T.; Hancke, K. 2015: Predicting fatigue in older breast cancer patients receiving radiotherapy. a head-to-head comparison of established assessments. Zeitschrift für Gerontologie und Geriatrie 48(2): 128-134
Hallmann, J.; Kolossa, S.; Gedrich, K.; Celis-Morales, C.; Forster, H.; O'Donovan, C.B.; Woolhead, C.; Macready, A.L.; Fallaize, R.; Marsaux, C.F.M.; Lambrinou, C.-P.; Mavrogianni, C.; Moschonis, G.; Navas-Carretero, S.; San-Cristobal, R.; Godlewska, M.; Surwiłło, A.; Mathers, J.C.; Gibney, E.R.; Brennan, L.; Walsh, M.C.; Lovegrove, J.A.; Saris, W.H.M.; Manios, Y.; Martinez, J.A.; Traczyk, I.; Gibney, M.J.; Daniel, H. 2015: Predicting fatty acid profiles in blood based on food intake and the FADS1 rs174546 SNP. Molecular Nutrition and Food Research 59(12): 2565-2573
Swan, M.; Christie, F.; Sitters, H.; York, A.; Di Stefano, J. 2015: Predicting faunal fire responses in heterogeneous landscapes: the role of habitat structure. Ecological Applications: a Publication of the Ecological Society of America 25(8): 2293-2305
Pettus, P.; Foster, E.; Pan, Y. 2015: Predicting fecal indicator organism contamination in Oregon coastal streams. Environmental Pollution 207: 68-78
Casanovas-Massana, A.; Gómez-Doñate, M.; Sánchez, D.; Belanche-Muñoz, L.ís.A.; Muniesa, M.; Blanch, A.R. 2015: Predicting fecal sources in waters with diverse pollution loads using general and molecular host-specific indicators and applying machine learning methods. Journal of Environmental Management 151: 317-325
Bastianelli, D.; Bonnal, L.; Jaguelin-Peyraud, Y.; Noblet, J. 2015: Predicting feed digestibility from NIRS analysis of pig faeces. Animal: An International Journal of Animal Bioscience 9(5): 781-786
Lavo, J.Patrick.; Ludlow, D.; Morgan, M.; Caldito, G.; Nathan, C-Ann. 2017: Predicting feeding tube and tracheotomy dependence in laryngeal cancer patients. Acta Oto-Laryngologica 137(3): 326-330
Ross, M.G.; Amaya, K. 2015: Predicting fetal acidemia using umbilical venous cord gas parameters. Obstetrics and Gynecology 125(3): 741
Namburete, A.I.L.; Yaqub, M.; Kemp, B.; Papageorghiou, A.T.; Noble, J.A. 2014: Predicting fetal neurodevelopmental age from ultrasound images. Medical Image Computing and Computer-Assisted Intervention: Miccai . International Conference on Medical Image Computing and Computer-Assisted Intervention 17(Part 2): 260-267
DeRolph, C.R.; Nelson, S.A.C.; Kwak, T.J.; Hain, E.F. 2015: Predicting fine-scale distributions of peripheral aquatic species in headwater streams. Ecology and Evolution 5(1): 152-163
Lord, S.; Galna, B.; Yarnall, A.J.; Coleman, S.; Burn, D.; Rochester, L. 2016: Predicting first fall in newly diagnosed Parkinson's disease: Insights from a fall-naïve cohort. Movement Disorders: Official Journal of the Movement Disorder Society 31(12): 1829-1836
LeMoult, J.; Ordaz, S.J.; Kircanski, K.; Singh, M.K.; Gotlib, I.H. 2015: Predicting first onset of depression in young girls: Interaction of diurnal cortisol and negative life events. Journal of Abnormal Psychology 124(4): 850-859
Erickson, A.M.; Henry, B.I.; Murray, J.M.; Klasse, P.Johan.; Angstmann, C.N. 2015: Predicting first traversal times for virions and nanoparticles in mucus with slowed diffusion. Biophysical Journal 109(1): 164-172
Lu, C.-H.; Yu, C.-S.; Lin, Y.-F.; Chen, J.-Y. 2015: Predicting flavin and nicotinamide adenine dinucleotide-binding sites in proteins using the fragment transformation method. Biomed Research International 2015: 402536
Saxena, R.; Durward, A.; Steeley, S.; Murdoch, I.A.; Tibby, S.M. 2015: Predicting fluid responsiveness in 100 critically ill children: the effect of baseline contractility. Intensive Care Medicine 41(12): 2161-2169
Lane, S.H.; Hines, A.; Krowchuk, H. 2015: Predicting folic acid intake among college students. MCN. American journal of maternal child nursing 40(1): 51-57
Lundorff, P.; Brölmann, H.; Koninckx, P.R.; Mara, M.; Wattiez, A.; Wallwiener, M.; Trew, G.; Crowe, A.M.; De Wilde, R.L. 2015: Predicting formation of adhesions after gynaecological surgery: development of a risk score. Archives of Gynecology and Obstetrics 292(4): 931-938
Aw, D.; Thain, J.; Ali, A.; Aung, T.; Chua, W.M.; Sahota, O.; Weerasuriya, N.; Marshall, L.; Kearney, F.; Masud, T. 2016: Predicting fracture risk in osteoporosis: the use of fracture prediction tools in an osteoporosis clinic population. Postgraduate Medical Journal 92(1087): 267-270
Rollemberg, C.V.V.; Silva, M.íl.M.B.L.; Rollemberg, K.C.; Amorim, F.áb.R.; Lessa, N.M.N.; Santos, M.D.S.; Souza, A.ác.M.B.; Melo, E.V.; Almeida, R.P.; Silva, Ân.M.; Werneck, G.L.; Santos, M.A.; Almeida, J.é A.P.; Jesus, A.él.R. 2015: Predicting frequency distribution and influence of sociodemographic and behavioral risk factors of Schistosoma mansoni infection and analysis of co-infection with intestinal parasites. Geospatial Health 10(1): 303
Kerkhof, M.; Freeman, D.; Jones, R.; Chisholm, A.; Price, D.B. 2015: Predicting frequent COPD exacerbations using primary care data. International Journal of Chronic Obstructive Pulmonary Disease 10: 2439-2450
Grinspan, Z.M.; Shapiro, J.S.; Abramson, E.L.; Hooker, G.; Kaushal, R.; Kern, L.M. 2015: Predicting frequent ED use by people with epilepsy with health information exchange data. Neurology 85(12): 1031-1038
Price, D.; Wilson, A.M.; Chisholm, A.; Rigazio, A.; Burden, A.; Thomas, M.; King, C. 2016: Predicting frequent asthma exacerbations using blood eosinophil count and other patient data routinely available in clinical practice. Journal of Asthma and Allergy 9: 1-12
Low, L.Leng.; Liu, N.; Wang, S.; Thumboo, J.; Ong, M.Eng.Hock.; Lee, K.Hock. 2016: Predicting frequent hospital admission risk in Singapore: a retrospective cohort study to investigate the impact of comorbidities, acute illness burden and social determinants of health. Bmj Open 6(10): E012705
Siwo, G.H.; Tan, A.; Button-Simons, K.A.; Samarakoon, U.; Checkley, L.A.; Pinapati, R.S.; Ferdig, M.T. 2015: Predicting functional and regulatory divergence of a drug resistance transporter gene in the human malaria parasite. Bmc Genomics 16: 115
Messé, A.; Rudrauf, D.; Giron, A.; Marrelec, G. 2015: Predicting functional connectivity from structural connectivity via computational models using MRI: an extensive comparison study. Neuroimage 111: 65-75
Lowthian, J.A.; Straney, L.D.; Brand, C.A.; Barker, A.; Smit, P.d.V.; Newnham, H.; Hunter, P.; Smith, C.; Cameron, P.A. 2017: Predicting functional decline in older emergency patients-the Safe Elderly Emergency Discharge (SEED) project. Age and Ageing 46(2): 219-225
Ferroli, P.; Broggi, M.; Schiavolin, S.; Acerbi, F.; Bettamio, V.; Caldiroli, D.; Cusin, A.; La Corte, E.; Leonardi, M.; Raggi, A.; Schiariti, M.; Visintini, S.; Franzini, A.; Broggi, G. 2015: Predicting functional impairment in brain tumor surgery: the Big Five and the Milan Complexity Scale. Neurosurgical Focus 39(6): E14
Ntaios, G.; Papavasileiou, V.; Michel, P.; Tatlisumak, T.; Strbian, D. 2015: Predicting functional outcome and symptomatic intracranial hemorrhage in patients with acute ischemic stroke: a glimpse into the crystal ball?. Stroke 46(3): 899-908
Flores Saiffe Farías, A.; Jaime Herrera López, E.; Moreno Vázquez, C.J.; Li, W.; Prado Montes de Oca, E. 2015: Predicting functional regulatory SNPs in the human antimicrobial peptide genes DEFB1 and CAMP in tuberculosis and HIV/AIDS. Computational Biology and Chemistry 59 Pt A: 117-125
Valencia, M.; Fresán, A.; Barak, Y.; Juárez, F.; Escamilla, R.; Saracco, R. 2015: Predicting functional remission in patients with schizophrenia: a cross-sectional study of symptomatic remission, psychosocial remission, functioning, and clinical outcome. Neuropsychiatric Disease and Treatment 11: 2339-2348
Akita, T.; Tanaka, J.; Ohisa, M.; Sugiyama, A.; Nishida, K.; Inoue, S.; Shirasaka, T. 2016: Predicting future blood supply and demand in Japan with a Markov model: application to the sex- and age-specific probability of blood donation. Transfusion 56(11): 2750-2759
Danese, S.; Peyrin-Biroulet, L.; Fiorino, G. 2015: Predicting future disease course in Crohn's disease by colonoscopy or magnetic resonance: which is the crystal ball?. Gut 64(9): 1347-1348
Wu, D.J.; Hipolito, E.; Bilderback, A.; Okelo, S.O.; Garro, A. 2016: Predicting future emergency department visits and hospitalizations for asthma using the Pediatric Asthma Control and Communication Instrument - Emergency Department version (PACCI-ED). Journal of asthma: official journal of the Association for the Care of Asthma 53(4): 387-391
Szczesniak, R.D.; McPhail, G.L.; Li, D.; Amin, R.S.; Clancy, J.P. 2016: Predicting future lung function decline in cystic fibrosis patients: Statistical methods and clinical connections. Pediatric Pulmonology 51(2): 217-218
Cook, C. 2016: Predicting future physical injury in sports: it's a complicated dynamic system. British Journal of Sports Medicine 50(22): 1356-1357
Hu, C.; Harber, P.; Su, J. 2016: Predicting future protection of respirator users: Statistical approaches and practical implications. Journal of Occupational and Environmental Hygiene 13(5): 393-400
Marras, S.; Cucco, A.; Antognarelli, F.; Azzurro, E.; Milazzo, M.; Bariche, M.; Butenschön, M.; Kay, S.; Di Bitetto, M.; Quattrocchi, G.; Sinerchia, M.; Domenici, P. 2015: Predicting future thermal habitat suitability of competing native and invasive fish species: from metabolic scope to oceanographic modelling. Conservation Physiology 3(1): Cou059
Van den Biggelaar, A.H.J.; Poolman, J.T. 2016: Predicting future trends in the burden of pertussis in the 21st century: implications for infant pertussis and the success of maternal immunization. Expert Review of Vaccines 15(1): 69-80
Li, A.; Gao, J.; Freels, S.; Huang, J.; Yu, G. 2016: Predicting gas chromatography relative retention times for polychlorinated biphenyls using chlorine substitution pattern contribution method. Journal of Chromatography. a 1427: 161-169
Fournel, S.; Marcos, B.; Godbout, S.; Heitz, M. 2015: Predicting gaseous emissions from small-scale combustion of agricultural biomass fuels. Bioresource Technology 179: 165-172
Lehto, E.; Ray, C.; Haukkala, A.; Yngve, A.; Thorsdottir, I.; Roos, E. 2015: Predicting gender differences in liking for vegetables and preference for a variety of vegetables among 11-year-old children. Appetite 95: 285-292
Zhang, L.-Q.; Li, Q.-Z.; Su, W.-X.; Jin, W. 2016: Predicting gene expression level by the transcription factor binding signals in human embryonic stem cells. Bio Systems 150: 92-98
Reddy, C.K.; Aziz, M.S. 2015: Predicting gene functions from multiple biological sources using novel ensemble methods. International Journal of Data Mining and Bioinformatics 12(2): 184-206
Belinsky, S.A.; Grimes, M.; Johnson, D.; Levy, D.; Schiller, J. 2016: Predicting gene promoter methylation in lung tumors through examination of sputum and serum. Journal of Clinical Oncology 24(18_Suppl): 7208-7208
Pavlides, J.M.Whitehead.; Zhu, Z.; Gratten, J.; McRae, A.F.; Wray, N.R.; Yang, J. 2016: Predicting gene targets from integrative analyses of summary data from GWAS and eQTL studies for 28 human complex traits. Genome Medicine 8(1): 84
Gibbons, A.; Groarke, A.; Sweeney, K. 2016: Predicting general and cancer-related distress in women with newly diagnosed breast cancer. Bmc Cancer 16(1): 935
Pflueger, M.O.; Franke, I.; Graf, M.; Hachtel, H. 2015: Predicting general criminal recidivism in mentally disordered offenders using a random forest approach. Bmc Psychiatry 15: 62
Ngune, I.; Jiwa, M.; McManus, A.; Parsons, R.; Hodder, R. 2015: Predicting general practice attendance for follow-up cancer care. American Journal of Health Behavior 39(2): 167-174
Ruckerbauer, D.E.; Jungreuthmayer, C.; Zanghellini, J.ür. 2015: Predicting genetic engineering targets with Elementary Flux Mode Analysis: a review of four current methods. New Biotechnology 32(6): 534-546
Calzone, L.; Barillot, E.; Zinovyev, A. 2015: Predicting genetic interactions from Boolean models of biological networks. Integrative Biology: Quantitative Biosciences from Nano to Macro 7(8): 921-929
Li, P.; Ma, H.; Zhao, X.; Chen, T. 2016: Predicting genetic modification targets based on metabolic network analysis--a review. Sheng Wu Gong Cheng Xue Bao 32(1): 1-13
Bagalà, P.; Ingrosso, G.; Falco, M.D.; Petrichella, S.; D'Andrea, M.; Rago, M.; Lancia, A.; Bruni, C.; Ponti, E.; Santoni, R. 2016: Predicting genitourinary toxicity in three-dimensional conformal radiotherapy for localized prostate cancer: a dose-volume parameters analysis of the bladder. Journal of Cancer Research and Therapeutics 12(2): 1018-1024
Suzuki, Y.; Doan, Y.H.; Kimura, H.; Shinomiya, H.; Shirabe, K.; Katayama, K. 2016: Predicting genotype compositions in norovirus seasons in Japan. Microbiology and Immunology 60(6): 418-426
Ryckman, K.K.; Berberich, S.L.; Dagle, J.M. 2016: Predicting gestational age using neonatal metabolic markers. American Journal of Obstetrics and Gynecology 214(4): 515.E1-515.E13
Alptekin, H.üs.ü; Çizmecioğlu, A.; Işık, H.; Cengiz, T.ür.; Yildiz, M.; Iyisoy, M.S. 2016: Predicting gestational diabetes mellitus during the first trimester using anthropometric measurements and HOMA-IR. Journal of Endocrinological Investigation 39(5): 577-583
Kessler, T. 2016: Predicting glioblastoma response to bevacizumab through marker profiling?. Neuro-Oncology 18(2): 149-150
Barabás, G.ör.; Allesina, S. 2015: Predicting global community properties from uncertain estimates of interaction strengths. Journal of the Royal Society Interface 12(109): 20150218
Jensen, P.M.; De Fine Licht, H.H. 2016: Predicting global variation in infectious disease severity: a bottom-up approach. Evolution Medicine and Public Health 1: 85-94
Schumacher, K.R.; Almond, C.; Singh, T.P.; Kirk, R.; Spicer, R.; Hoffman, T.M.; Hsu, D.; Naftel, D.C.; Pruitt, E.; Zamberlan, M.; Canter, C.E.; Gajarski, R.J. 2015: Predicting graft loss by 1 year in pediatric heart transplantation candidates: an analysis of the Pediatric Heart Transplant Study database. Circulation 131(10): 890-898
Osis, S.T.; Hettinga, B.A.; Ferber, R. 2016: Predicting ground contact events for a continuum of gait types: An application of targeted machine learning using principal component analysis. Gait and Posture 46: 86-90
Close, M.E.; Abraham, P.; Humphries, B.; Lilburne, L.; Cuthill, T.; Wilson, S. 2016: Predicting groundwater redox status on a regional scale using linear discriminant analysis. Journal of Contaminant Hydrology 191: 19-32
Bassim, S.; Chapman, R.W.; Tanguy, A.; Moraga, D.; Tremblay, R. 2015: Predicting growth and mortality of bivalve larvae using gene expression and supervised machine learning. Comparative Biochemistry and Physiology. Part D Genomics and Proteomics 16: 59-72
Sridhara, V.; Meyer, A.G.; Rai, P.; Barrick, J.E.; Ravikumar, P.; Segrè, D.; Wilke, C.O. 2014: Predicting growth conditions from internal metabolic fluxes in an in-silico model of E. coli. Plos one 9(12): E114608
Baranowski, T.; Chen, T-An.; O'Connor, T.M.; Hughes, S.O.; Diep, C.S.; Beltran, A.; Brand, L.; Nicklas, T.; Baranowski, J. 2016: Predicting habits of vegetable parenting practices to facilitate the design of change programmes. Public Health Nutrition 19(11): 1976-1982
Tewarie, P.; Bright, M.G.; Hillebrand, A.; Robson, S.E.; Gascoyne, L.E.; Morris, P.G.; Meier, J.; Van Mieghem, P.; Brookes, M.J. 2016: Predicting haemodynamic networks using electrophysiology: the role of non-linear and cross-frequency interactions. Neuroimage 130: 273-292
Van Mourik, S.; Alders, B.P.G.J.; Helderman, F.; van de Ven, L.J.F.; Groot Koerkamp, P.W.G. 2017: Predicting hairline fractures in eggs of mature hens. Poultry Science 96(6): 1956-1962
Paek, A.Y.; Gailey, A.; Parikh, P.; Santello, M.; Contreras-Vidal, J. 2015: Predicting hand forces from scalp electroencephalography during isometric force production and object grasping. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2015: 7570-7573
Küpper, H.; Kudernatsch, M.; Pieper, T.; Groeschel, S.; Tournier, J.-D.; Raffelt, D.; Winkler, P.; Holthausen, H.; Staudt, M. 2016: Predicting hand function after hemidisconnection. Brain: a Journal of Neurology 139(Part 9): 2456-2468
Liu, C.-J.; Marie, D.; Fredrick, A.; Bertram, J.; Utley, K.; Fess, E.E. 2017: Predicting hand function in older adults: evaluations of grip strength, arm curl strength, and manual dexterity. Aging Clinical and Experimental Research 29(4): 753-760
Zhang, P.; Ma, X.; Huang, H.; He, J. 2014: Predicting hand orientation in reach-to-grasp tasks using neural activities from primary motor cortex. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2014: 1306-1309
Biffani, S.; Dimauro, C.; Macciotta, N.ò; Rossoni, A.; Stella, A.; Biscarini, F. 2015: Predicting haplotype carriers from SNP genotypes in Bos taurus through linear discriminant analysis. Genetics Selection Evolution: Gse 47: 4
Chen, M.-F.; Wang, R.-H.; Hung, S.-L. 2015: Predicting health-promoting self-care behaviors in people with pre-diabetes by applying Bandura social learning theory. Applied Nursing Research: Anr 28(4): 299-304
Ferriz, R.; González-Cutre, D.; Sicilia, Á; Hagger, M.S. 2016: Predicting healthy and unhealthy behaviors through physical education: a self-determination theory-based longitudinal approach. Scandinavian Journal of Medicine and Science in Sports 26(5): 579-592
Lin, L.; Jin, C.; Fu, Z.; Zhang, B.; Bin, G.; Wu, S. 2016: Predicting healthy older adult's brain age based on structural connectivity networks using artificial neural networks. Computer Methods and Programs in Biomedicine 125: 8-17
Hawkins, N.M.; Virani, S.A.; Sperrin, M.; Buchan, I.E.; McMurray, J.J.V.; Krahn, A.D. 2016: Predicting heart failure decompensation using cardiac implantable electronic devices: a review of practices and challenges. European Journal of Heart Failure 18(8): 977-986
Hu, Q.; Xiao, Z.; Xiong, X.; Zhou, G.; Guan, X. 2015: Predicting heavy metals' adsorption edges and adsorption isotherms on MnO2 with the parameters determined from Langmuir kinetics. Journal of Environmental Sciences 27: 207-216
Shahdoust, M.; Sadeghifar, M.; Poorolajal, J.; Javanrooh, N.; Amini, P. 2015: Predicting hepatitis B monthly incidence rates using weighted Markov chains and time series methods. Journal of Research in Health Sciences 15(1): 28-31
Liu, J.; Mansouri, K.; Judson, R.S.; Martin, M.T.; Hong, H.; Chen, M.; Xu, X.; Thomas, R.S.; Shah, I. 2015: Predicting hepatotoxicity using ToxCast in vitro bioactivity and chemical structure. Chemical Research in Toxicology 28(4): 738-751
Amuzu-Aweh, E.N.; Bovenhuis, H.; de Koning, D.-J.; Bijma, P. 2015: Predicting heterosis for egg production traits in crossbred offspring of individual White Leghorn sires using genome-wide SNP data. Genetics Selection Evolution: Gse 47: 27
Matharu, G.S.; Berryman, F.; Brash, L.; Pynsent, P.B.; Treacy, R.B.C.; Dunlop, D.J. 2015: Predicting high blood metal ion concentrations following hip resurfacing. Hip International: the Journal of Clinical and Experimental Research on Hip Pathology and Therapy 25(6): 510-519
Fischer, R.; Dreisbach, G. 2015: Predicting high levels of multitasking reduces between-tasks interactions. Journal of Experimental Psychology. Human Perception and Performance 41(6): 1482-1487
Leininger, L.J.; Saloner, B.; Wherry, L.R. 2015: Predicting high-cost pediatric patients: derivation and validation of a population-based model. Medical Care 53(8): 729-735
Bryant, R.J.; Sjoberg, D.D.; Vickers, A.J.; Robinson, M.C.; Kumar, R.; Marsden, L.; Davis, M.; Scardino, P.T.; Donovan, J.; Neal, D.E.; Lilja, H.; Hamdy, F.C. 2015: Predicting high-grade cancer at ten-core prostate biopsy using four kallikrein markers measured in blood in the ProtecT study. Journal of the National Cancer Institute 107(7)
Kaplan, N.M. 2016: Predicting home-clinic blood pressure differences. Journal of the American Society of Hypertension: Jash 10(3): 186
Ireland, J.L.; Priday, L.J.; Ireland, C.A.; Chu, S.; Kilcoyne, J.; Mulligan, C. 2016: Predicting hospital aggression in secure psychiatric care. Bjpsych Open 2(1): 96-100
Li, B.; Cairns, J.; Fotheringham, J.; Ravanan, R. 2016: Predicting hospital costs for patients receiving renal replacement therapy to inform an economic evaluation. European Journal of Health Economics: Hepac: Health Economics in Prevention and Care 17(6): 659-668
Agarwal, V.; Han, L.; Madan, I.; Saluja, S.; Shidham, A.; Shah, N.H. 2016: Predicting hospital visits from geo-tagged Internet search logs. AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science 2016: 15-24
Uszko-Lencer, N.H.M.K.; Frankenstein, L.; Spruit, M.A.; Maeder, M.T.; Gutmann, M.; Muzzarelli, S.; Osswald, S.; Pfisterer, M.E.; Zugck, C.; Brunner-La Rocca, H.-P. 2017: Predicting hospitalization and mortality in patients with heart failure: the BARDICHE-index. International Journal of Cardiology 227: 901-907
Chuang, W.-C.; Gober, P. 2015: Predicting hospitalization for heat-related illness at the census-tract level: accuracy of a generic heat vulnerability index in Phoenix, Arizona (USA). Environmental Health Perspectives 123(6): 606-612
Eng, C.L.P.; Tong, J.C.; Tan, T.W. 2014: Predicting host tropism of influenza a virus proteins using random forest. Bmc Medical Genomics 7(Suppl 3): S1
Xia, J.; Yue, Z.; Di, Y.; Zhu, X.; Zheng, C.-H. 2016: Predicting hot spots in protein interfaces based on protrusion index, pseudo hydrophobicity and electron-ion interaction pseudopotential features. Oncotarget 7(14): 18065-18075
Bailey, J.; Thew, M.; Balls, M. 2015: Predicting human drug toxicity and safety via animal tests: can any one species predict drug toxicity in any other, and do monkeys help?. Alternatives to Laboratory Animals: Atla 43(6): 393-403
Chowdhury, S. 2015: Predicting human exposure and risk from chlorinated indoor swimming pool: a case study. Environmental Monitoring and Assessment 187(8): 502
Lu, X.; Megchelenbrink, W.; Notebaart, R.A.; Huynen, M.A. 2015: Predicting human genetic interactions from cancer genome evolution. Plos one 10(5): E0125795
Waters, L.J.; Shokry, D.S.; Parkes, G.M.B. 2016: Predicting human intestinal absorption in the presence of bile salt with micellar liquid chromatography. Biomedical Chromatography: Bmc 30(10): 1618-1624
Basant, N.; Gupta, S.; Singh, K.P. 2016: Predicting human intestinal absorption of diverse chemicals using ensemble learning based QSAR modeling approaches. Computational Biology and Chemistry 61: 178-196
Li, J.; Fu, J.; Huang, X.; Lu, D.; Wu, J. 2016: Predicting hydration free energies of amphetamine-type stimulants with a customized molecular model. Journal of Physics. Condensed Matter: An Institute of Physics Journal 28(34): 344001
Wedler, H.B.; Palazzo, T.A.; Pemberton, R.P.; Hamann, C.S.; Kurth, M.J.; Tantillo, D.J. 2015: Predicting hydration propensities of biologically relevant α-ketoamides. Bioorganic and Medicinal Chemistry Letters 25(19): 4153-4157
Dunitz, M.; Verghese, G.; Heldt, T. 2015: Predicting hyperlactatemia in the MIMIC Ii database. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2015: 985-988
Bonafede, M.; Shi, N.; Barron, R.; Li, X.; Crittenden, D.B.; Chandler, D. 2016: Predicting imminent risk for fracture in patients aged 50 or older with osteoporosis using US claims data. Archives of Osteoporosis 11(1): 26
Pope, J.E. 2016: Predicting improvement in diffuse scleroderma: lessons learnt. Annals of the Rheumatic Diseases 75(10): 1741-1742
Kim, S.; Katchooi, M.; Bayiri, B.; Sarikaya, M.; Korpak, A.M.; Huang, G.J. 2016: Predicting improvement of postorthodontic white spot lesions. American Journal of Orthodontics and Dentofacial Orthopedics: Official Publication of the American Association of Orthodontists its Constituent Societies and the American Board of Orthodontics 149(5): 625-633
Dong, R.; Zhao, G. 2014: Predicting in vitro rumen VFA production using CNCPS carbohydrate fractions with multiple linear models and artificial neural networks. Plos one 9(12): E116290
Hormuth, D.A.; Weis, J.A.; Barnes, S.L.; Miga, M.I.; Rericha, E.C.; Quaranta, V.; Yankeelov, T.E. 2015: Predicting in vivo glioma growth with the reaction diffusion equation constrained by quantitative magnetic resonance imaging data. Physical Biology 12(4): 046006
Bauch, C.; Bevan, S.; Woodhouse, H.; Dilworth, C.; Walker, P. 2015: Predicting in vivo phospholipidosis-inducing potential of drugs by a combined high content screening and in silico modelling approach. Toxicology in Vitro: An International Journal Published in Association with Bibra 29(3): 621-630
Bakhshayesh, B.; Hosseininezhad, M.; Seyed Saadat, S.M.; Hajmanuchehri, M.; Kazemnezhad, E.; Ghayeghran, A.-R. 2014: Predicting in-hospital mortality in Iranian patients with spontaneous intracerebral hemorrhage. Iranian Journal of Neurology 13(4): 231-236
Van Belleghem, G.; Devos, S.; De Wit, L.; Hubloue, I.; Lauwaert, D.; Pien, K.; Putman, K. 2016: Predicting in-hospital mortality of traffic victims: A comparison between AIS-and ICD-9-CM-related injury severity scales when only ICD-9-CM is reported. Injury 47(1): 141-146
Allen, L.A.; McIlvennan, C.K. 2016: Predicting in-hospital worsening heart failure at time of admission, but do we really need another heart failure risk model?. American Heart Journal 178: 188-189
Dik, V.K.; Moons, L.M.G.; Hüyük, M.; van der Schaar, P.; de Vos Tot Nederveen Cappel, W.H.; Ter Borg, P.C.J.; Meijssen, M.A.C.; Ouwendijk, R.J.T.H.; Le Fèvre, D.M.; Stouten, M.; van der Galiën, O.; Hiemstra, T.J.; Monkelbaan, J.F.; van Oijen, M.G.H.; Siersema, P.D.; Moons, L.M.G.; van der Schaar, P.; de Vos Tot Nederveen Cappel, W.H.; Ter Borg, P.C.J.; Tang, T.J.; Ter Borg, F.; Meijssen, M.A.C.; Ouwendijk, R.J.T.H.; Le Fèvre, D.M.; Stouten, M.; van der Galiën, O.; Hiemstra, T.J.; Kuipers, E.J.; Siersema, P.D. 2015: Predicting inadequate bowel preparation for colonoscopy in participants receiving split-dose bowel preparation: development and validation of a prediction score. Gastrointestinal Endoscopy 81(3): 665-672
Basta, M.N.; Mirzabeigi, M.N.; Shubinets, V.; Kelz, R.R.; Williams, N.N.; Fischer, J.P. 2016: Predicting incisional hernia after bariatric surgery: a risk stratification model based upon 2161 operations. Surgery for Obesity and Related Diseases: Official Journal of the American Society for Bariatric Surgery 12(8): 1466-1473
Qin, J.; Chen, S.-G.; Hu, D.; Zeng, L.-L.; Fan, Y.-M.; Chen, X.-P.; Shen, H. 2015: Predicting individual brain maturity using dynamic functional connectivity. Frontiers in Human Neuroscience 9: 418
Wise, E.A.; Streiner, D.L.; Gallop, R.J. 2016: Predicting individual change during the course of treatment. PsychoTherapy Research: Journal of the Society for PsychoTherapy Research 26(5): 623-631
Puhan, M.A. 2016: Predicting individual lung-function trajectories: An opportunity for prevention?. Cmaj: Canadian Medical Association Journal 188(14): 997-998
Meng, X.; Jiang, R.; Lin, D.; Bustillo, J.; Jones, T.; Chen, J.; Yu, Q.; Du, Y.; Zhang, Y.; Jiang, T.; Sui, J.; Calhoun, V.D. 2017: Predicting individualized clinical measures by a generalized prediction framework and multimodal fusion of MRi data. Neuroimage 145(Part B): 218-229
Rekik, I.; Li, G.; Lin, W.; Shen, D. 2016: Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing. Medical Image Analysis 28: 1-12
Meira, K.ás.R.S.õe.; de Mattos Brito, C.S.; de Sousa, F.B. 2015: Predicting infiltration of the surface layer of natural enamel caries. Archives of Oral Biology 60(6): 883-893
Moniz, L.; Buczak, A.L.; Baugher, B.; Guven, E.; Chretien, J.-P. 2016: Predicting influenza with dynamical methods. Bmc Medical Informatics and Decision Making 16(1): 134
Bousardt, A.M.C.; Hoogendoorn, A.W.; Noorthoorn, E.O.; Hummelen, J.W.; Nijman, H.L.I. 2016: Predicting inpatient aggression by self-reported impulsivity in forensic psychiatric patients. Criminal Behaviour and Mental Health: Cbmh 26(3): 161-173
Chen, J.H.; Goldstein, M.K.; Asch, S.M.; Mackey, L.; Altman, R.B. 2017: Predicting inpatient clinical order patterns with probabilistic topic models vs conventional order sets. Journal of the American Medical Informatics Association: JAMIA 24(3): 472-480
Smith, M.W.; Friedman, B.; Karaca, Z.; Wong, H.S. 2015: Predicting inpatient hospital payments in the United States: a retrospective analysis. Bmc Health Services Research 15: 372
Liang, M.I.; Rosen, M.A.; Rath, K.S.; Hade, E.M.; Clements, A.E.; Backes, F.J.; Eisenhauer, E.L.; Salani, R.; O'Malley, D.M.; Fowler, J.M.; Cohn, D.E. 2015: Predicting inpatient stay lasting 2 midnights or longer after robotic surgery for endometrial cancer. Journal of Minimally Invasive Gynecology 22(4): 583-589
Fisher, C.; Karalapillai, D.K.; Bailey, M.; Glassford, N.G.; Bellomo, R.; Jones, D. 2015: Predicting intensive care and hospital outcome with the Dalhousie Clinical Frailty Scale: a pilot assessment. Anaesthesia and Intensive Care 43(3): 361-368
Sainsbury, K.; Mullan, B.; Sharpe, L. 2015: Predicting intention and behaviour following participation in a theory-based intervention to improve gluten free diet adherence in coeliac disease. Psychology and Health 30(9): 1063-1074
Akbar, H.; Anderson, D.; Gallegos, D. 2015: Predicting intentions and behaviours in populations with or at-risk of diabetes: a systematic review. Preventive Medicine Reports 2: 270-282
Chung, M.-H.; Ho, C.-H.; Wen, H.-C. 2016: Predicting intentions of nurses to adopt patient personal health records: a structural equation modeling approach. Computer Methods and Programs in Biomedicine 136: 45-53
Sun, H.; Wang, Y.; Zhang, Z.; Liu, L.; Yang, P. 2015: Predicting interatrial septum rotation: is the position of the heart or the direction of the coronary sinus reliable?: Implications for interventional electrophysiologists from CT studies. Pacing and clinical electrophysiology: PACE 38(4): 514-519
Schultz, A.é; Qutub, A.A. 2015: Predicting internal cell fluxes at sub-optimal growth. Bmc Systems Biology 9: 18
Sabdia, S.; Greer, R.M.; Prior, T.; Kumar, S. 2015: Predicting intrapartum fetal compromise using the fetal cerebro-umbilical ratio. Placenta 36(5): 594-598
Fan, L.; Lin, B.; Xu, T.; Xia, N.; Shao, X.; Tan, X.; Zhong, M.; Yang, Y.; Zhao, B. 2017: Predicting intraprocedural rupture and thrombus formation during coiling of ruptured anterior communicating artery aneurysms. Journal of Neurointerventional Surgery 9(4): 370-375
Qin, Z.; Zhang, J.-e.; DiTommaso, A.; Wang, R.-l.; Wu, R.-s. 2015: Predicting invasions of Wedelia trilobata (L.) Hitchc. with Maxent and GARP models. Journal of Plant Research 128(5): 763-775
Shahin, J.; Allen, E.J.; Patel, K.; Muskett, H.; Harvey, S.E.; Edgeworth, J.; Kibbler, C.C.; Barnes, R.A.; Biswas, S.; Soni, N.; Rowan, K.M.; Harrison, D.A. 2016: Predicting invasive fungal disease due to Candida species in non-neutropenic, critically ill, adult patients in United Kingdom critical care units. Bmc Infectious Diseases 16: 480
Yaqubi, M.; Mohammadnia, A.; Fallahi, H. 2015: Predicting involvement of polycomb repressive complex 2 in direct conversion of mouse fibroblasts into induced neural stem cells. Stem Cell Research and Therapy 6: 42
Mandell, B.F. 2016: Predicting is tough, especially about the future. Cleveland Clinic Journal of Medicine 83(5): 324
Valuckiene, Z.; Budrys, P.; Jurkevicius, R. 2016: Predicting ischemic mitral regurgitation in patients with acute ST-elevation myocardial infarction: Does time to reperfusion really matter and what is the role of collateral circulation?. International Journal of Cardiology 203: 667-671
Raskevicius, V.; Kairys, V. 2017: Predicting Isoform-specific Binding Selectivities of Benzensulfonamides Using QSAR and 3D-QSAR. Current Computer-Aided Drug Design 13(1): 75-83
Wang, F.; Dörfler, U.; Jiang, X.; Schroll, R. 2016: Predicting isoproturon long-term mineralization from short-term experiment: can this be a suitable approach?. Chemosphere 144: 312-318
Schmitt, N.; Pulakos, E.D. 1985: Predicting job satisfaction from life satisfaction: is there a general satisfaction factor?. International Journal of Psychology: Journal International de Psychologie 20(2): 155-167
Kirino, Y.; Hama, M.; Takase-Minegishi, K.; Kunishita, Y.; Kishimoto, D.; Yoshimi, R.; Asami, Y.; Ihata, A.; Oba, M.S.; Tsunoda, S.; Ohno, S.; Ueda, A.; Takeno, M.; Ishigatsubo, Y. 2015: Predicting joint destruction in rheumatoid arthritis with power Doppler, anti-citrullinated peptide antibody, and joint swelling. Modern Rheumatology 25(6): 842-848
Balankura, T.; Qi, X.; Zhou, Y.; Fichthorn, K.A. 2016: Predicting kinetic nanocrystal shapes through multi-scale theory and simulation: Polyvinylpyrrolidone-mediated growth of Ag nanocrystals. Journal of Chemical Physics 145(14): 144106
Waring, R.H.; Coops, N.C. 2016: Predicting large wildfires across western North America by modeling seasonal variation in soil water balance. Climatic Change 135: 325-339
Choi, J.; Park, S.W.; Kim, J.; Park, J.; Kim, J.S. 2016: Predicting late enophthalmos: Differences between medial and inferior orbital wall fractures. Journal of Plastic Reconstructive and Aesthetic Surgery: Jpras 69(12): E238-E244
Hamad, R.; Rehkopf, D.H.; Kuan, K.Y.; Cullen, M.R. 2016: Predicting later life health status and mortality using state-level socioeconomic characteristics in early life. Ssm - Population Health 2: 269-276
Zohsel, K.; Baldus, C.; Schmidt, M.H.; Esser, G.ün.; Banaschewski, T.; Thomasius, R.; Laucht, M. 2016: Predicting later problematic cannabis use from psychopathological symptoms during childhood and adolescence: Results of a 25-year longitudinal study. Drug and Alcohol Dependence 163: 251-255
Bazzi, W.M.; Sjoberg, D.D.; Grasso, A.A.C.; Bernstein, M.; Parra, R.; Coleman, J.A. 2015: Predicting length of stay after robotic partial nephrectomy. International Urology and Nephrology 47(8): 1321-1325
Paul Wright, G.; Stilwell, K.; Johnson, J.; Hefty, M.T.; Chung, M.H. 2015: Predicting length of stay and conversion to open cholecystectomy for acute cholecystitis using the 2013 Tokyo Guidelines in a US population. Journal of Hepato-Biliary-Pancreatic Sciences 22(11): 795-801
Van der Hoop, J.M.; Corkeron, P.; Henry, A.G.; Knowlton, A.R.; Moore, M.J. 2017: Predicting lethal entanglements as a consequence of drag from fishing gear. Marine Pollution Bulletin 115(1-2): 91-104
Pattillo Garnham, A.; Guzmán Rojas, R.; Shek, K.L.; Dietz, H.P. 2017: Predicting levator avulsion from ICS POP-Q findings. International Urogynecology Journal 28(6): 907-911
Shen, W.; Cao, Y.; Cha, L.; Zhang, X.; Ying, X.; Zhang, W.; Ge, K.; Li, W.; Zhong, L. 2015: Predicting linear B-cell epitopes using amino acid anchoring pair composition. Biodata Mining 8: 14
Ochiai, T.; Nacher, J.C. 2016: Predicting link directionality in gene regulation from gene expression profiles using volatility-constrained correlation. Bio Systems 145: 9-18
Zheng, D.-X.; Meng, S.-C.; Liu, Q.-J.; Li, C.-T.; Shang, X.-D.; Zhu, Y.-S.; Bai, T.-J.; Xu, S.-M. 2016: Predicting liver metastasis of gastrointestinal tract cancer by diffusion-weighted imaging of apparent diffusion coefficient values. World Journal of Gastroenterology 22(10): 3031-3037
Potts, J.R.; Bastille-Rousseau, G.; Murray, D.L.; Schaefer, J.A.; Lewis, M.A. 2014: Predicting local and non-local effects of resources on animal space use using a mechanistic step selection model. Methods in Ecology and Evolution 5(3): 253-262
Wood, J.G.; Heywood, A.E.; Menzies, R.I.; McIntyre, P.B.; MacIntyre, C.R. 2015: Predicting localised measles outbreak potential in Australia. Vaccine 33(9): 1176-1181
Bradshaw, T.; Fu, R.; Bowen, S.; Zhu, J.; Forrest, L.; Jeraj, R. 2015: Predicting location of recurrence using FDG, FLT, and Cu-ATSM PET in canine sinonasal tumors treated with radiotherapy. Physics in medicine and biology 60(13): 5211-5224
Abdizadeh, H.; Atilgan, C. 2016: Predicting long term cooperativity and specific modulators of receptor interactions in human transferrin from dynamics within a single microstate. Physical Chemistry Chemical Physics: Pccp 18(11): 7916-7926
Sharma, P.K.; Chhatriwalla, A.K.; Cohen, D.J.; Jang, J.-S.; Baweja, P.; Gosch, K.; Jones, P.; Bach, R.G.; Arnold, S.V.; Spertus, J.A. 2017: Predicting long-term bleeding after percutaneous coronary intervention. Catheterization and Cardiovascular Interventions: Official Journal of the Society for Cardiac Angiography and Interventions 89(2): 199-206
Monden, R.; Stegeman, A.; Conradi, H.J.; de Jonge, P.; Wardenaar, K.J. 2016: Predicting long-term depression outcome using a three-mode principal component model for depression heterogeneity. Journal of Affective Disorders 189: 1-9
Honeybul, S.; Ho, K.M. 2016: Predicting long-term neurological outcomes after severe traumatic brain injury requiring decompressive craniectomy: a comparison of the CRASH and IMPACT prognostic models. Injury 47(9): 1886-1892
Wu, X.; Li, C.; Oberst, K.; Given, C. 2016: Predicting long-term nursing home transfer from MI choice waiver program. Geriatric Nursing 37(6): 446-452
Lu, H.-Y.; Li, T.-C.; Tu, Y.-K.; Tsai, J.-C.; Lai, H.-S.; Kuo, L.-T. 2015: Predicting long-term outcome after traumatic brain injury using repeated measurements of Glasgow Coma Scale and data mining methods. Journal of Medical Systems 39(2): 14
Rassovsky, Y.; Levi, Y.; Agranov, E.; Sela-Kaufman, M.; Sverdlik, A.; Vakil, E. 2015: Predicting long-term outcome following traumatic brain injury (TBI). Journal of Clinical and Experimental Neuropsychology 37(4): 354-366
Divchev, D.; Najjar, T.; Tillwich, F.; Rehders, T.; Palisch, H.; Nienaber, C.A. 2015: Predicting long-term outcomes of acute aortic dissection: a focus on gender. Expert Review of Cardiovascular Therapy 13(3): 325-331
Senthong, V.; Wu, Y.; Hazen, S.L.; Tang, W.H.W. 2017: Predicting long-term prognosis in stable peripheral artery disease with baseline functional capacity estimated by the Duke Activity Status Index. American Heart Journal 184: 17-25
Kliem, S.ör.; Weusthoff, S.; Hahlweg, K.; Baucom, K.J.W.; Baucom, B.R. 2015: Predicting long-term risk for relationship dissolution using nonparametric conditional survival trees. Journal of Family Psychology: Jfp: Journal of the Division of Family Psychology of the American Psychological Association 29(6): 807-817
Carluccio, G.; Bruno, M.; Collins, C.M. 2016: Predicting long-term temperature increase for time-dependent SAR levels with a single short-term temperature response. Magnetic Resonance in Medicine 75(5): 2195-2203
Shankar, S.; Gowthaman, K.; Uddin, M.S. 2016: Predicting long-term wear performance of hard-on-hard bearing couples: effect of cup orientation. Medical and Biological Engineering and Computing 54(10): 1541-1552
Santos, I.ês.; Mata, J.; Silva, M.N.; Sardinha, L.ís.B.; Teixeira, P.J. 2015: Predicting long-term weight loss maintenance in previously overweight women: a signal detection approach. Obesity 23(5): 957-964
Veron, S.; Davies, T.J.; Cadotte, M.W.; Clergeau, P.; Pavoine, S. 2017: Predicting loss of evolutionary history: Where are we?. Biological Reviews of the Cambridge Philosophical Society 92(1): 271-291
Backes, M.; Dorr, M.C.; Luitse, J.S.K.; Goslings, J.C.; Schepers, T. 2016: Predicting loss of height in surgically treated displaced intra-articular fractures of the calcaneus. International Orthopaedics 40(3): 513-518
Calverley, P.M.A. 2015: Predicting loss of lung function in healthy people. LANCET. Respiratory Medicine 3(8): 590-591
Suemitsu, K.; Iida, O.; Shiraki, T.; Suemitsu, S.; Murakami, M.; Miyamoto, M.; Izumi, M.; Nakanishi, T. 2016: Predicting loss of patency after forearm loop arteriovenous graft. Journal of Vascular Surgery 64(2): 395-401
Millar, A.C.; Lau, A.N.C.; Tomlinson, G.; Kraguljac, A.; Simel, D.L.; Detsky, A.S.; Lipscombe, L.L. 2016: Predicting low testosterone in aging men: a systematic review. Cmaj: Canadian Medical Association Journal 188(13): E321-E330
Burki, T.K. 2016: Predicting lung cancer prognosis using machine learning. LANCET. Oncology 17(10): E421
Campbell, J.; Franzen, A.; Van Landingham, C.; Lumpkin, M.; Crowell, S.; Meredith, C.; Loccisano, A.; Gentry, R.; Clewell, H. 2016: Predicting lung dosimetry of inhaled particleborne benzo[a]pyrene using physiologically based pharmacokinetic modeling. Inhalation Toxicology 28(11): 520-535
Abdollah, F.; Klett, D.E.; Sammon, J.D.; Dalela, D.; Sood, A.; Hsu, L.; Diaz, M.; Gupta, N.; Peabody, J.O.; Trinh, Q.-D.; Menon, M. 2016: Predicting lymph node invasion in patients treated with robot-assisted radical prostatectomy. Canadian Journal of Urology 23(1): 8141-8150
Agar, N.J.M.; Kirton, C.; Patel, R.S.; Martin, R.C.W.; Angelo, N.; Emanuel, P.O. 2015: Predicting lymph node metastases in cutaneous squamous cell carcinoma: use of a morphological scoring system. New Zealand Medical Journal 128(1411): 59-67
Lee, J.H.; Shin, H.J.; Yoon, J.H.; Kim, E.-K.; Moon, H.J.; Lee, H.S.; Kwon, H.J.; Kwak, J.Y. 2017: Predicting lymph node metastasis in patients with papillary thyroid carcinoma by vascular index on power Doppler ultrasound. Head and Neck 39(2): 334-340
Ilker, S.; Nilufer, C.; Firat, C.Z.; Bulent, O.; Hatice, B.; Tayfun, G. 2015: Predicting lympho-vascular space invasion in endometrial cancers with mucinous carcinomatous components. Asian Pacific Journal of Cancer Prevention: Apjcp 16(10): 4247-4250
Ju, Z.; Cao, J.-Z.; Gu, H. 2016: Predicting lysine phosphoglycerylation with fuzzy SVM by incorporating k-spaced amino acid pairs into Chou׳s general PseAAC. Journal of Theoretical Biology 397: 145-150
Ferreira, P.; Fonseca, N.A.; Dutra, I.ês.; Woods, R.; Burnside, E. 2015: Predicting malignancy from mammography findings and image-guided core biopsies. International Journal of Data Mining and Bioinformatics 11(3): 257-276
Witczak, J.; Taylor, P.; Chai, J.; Amphlett, B.; Soukias, J.-M.; Das, G.; Tennant, B.P.; Geen, J.; Okosieme, O.E. 2016: Predicting malignancy in thyroid nodules: feasibility of a predictive model integrating clinical, biochemical, and ultrasound characteristics. Thyroid Research 9: 4
Valdés, L.; San-José, E.; Ferreiro, L.ía.; Golpe, A.; González-Barcala, F.-J.; Toubes, M.ía.E.; Rodríguez-Álvarez, M.ía.X.; Álvarez-Dobaño, J.é M.; Rodríguez-Núñez, N.; Rábade, C.; Gude, F. 2015: Predicting malignant and tuberculous pleural effusions through demographics and pleural fluid analysis of patients. Clinical Respiratory Journal 9(2): 203-213
Chammas, M.C.; Macedo, T.úl.A.A.; Lo, V.W.; Gomes, A.C.; Juliano, A.; Cerri, G.G. 2016: Predicting malignant neck lymphadenopathy using color duplex sonography based on multivariate analysis. Journal of Clinical Ultrasound: Jcu 44(9): 587-594
Zhang, H.; Li, H.; Ma, Q.; Yang, F-Yan.; Diao, T-Yu. 2016: Predicting malignant transformation of esophageal squamous cell lesions by combined biomarkers in an endoscopic screening program. World Journal of Gastroenterology 22(39): 8770-8778
La Delfa, N.J.; Potvin, J.R. 2016: Predicting manual arm strength: a direct comparison between artificial neural network and multiple regression approaches. Journal of Biomechanics 49(4): 602-605
Bellenger, C.R.; Fuller, J.T.; Nelson, M.J.; Hartland, M.; Buckley, J.D.; Debenedictis, T.A. 2015: Predicting maximal aerobic speed through set distance time-trials. European Journal of Applied Physiology 115(12): 2593-2598
Grilo, C.M.; White, M.A.; Masheb, R.M.; Gueorguieva, R. 2015: Predicting meaningful outcomes to medication and self-help treatments for binge-eating disorder in primary care: The significance of early rapid response. Journal of Consulting and Clinical Psychology 83(2): 387-394
Fowler, S.M.; Schmidt, H.; van de Ven, R.; Wynn, P.; Hopkins, D.L. 2015: Predicting meat quality traits of ovine m. semimembranosus, both fresh and following freezing and thawing, using a hand held Raman spectroscopic device. Meat Science 108: 138-144
Paolino, N.D.; Artino, A.R.; Saguil, A.; Dong, T.; Durning, S.J.; DeZee, K.J. 2015: Predicting medical school and internship success: does the quality of the research and clinical experience matter?. Military medicine 180(4 Suppl): 12-17
Turner, A.P.; Roubinov, D.S.; Atkins, D.C.; Haselkorn, J.K. 2016: Predicting medication adherence in multiple sclerosis using telephone-based home monitoring. Disability and Health Journal 9(1): 83-89
Scott, J. 2000: Predicting medication non-adherence in severe affective disorders. Acta Neuropsychiatrica 12(3): 128-130
Trenerry, M.R.; Meador, K.J. 2015: Predicting memory change after temporal lobectomy for epilepsy. Neurology 84(15): 1508-1509
MacDonald, O.K.; D'Amico, A.V.; Sadetsky, N.; Shrieve, D.C.; Bakst, A.W.; Carroll, P.R. 2016: Predicting men at high risk for PSA failure after salvage radiotherapy for rising PSA following prostatectomy. Journal of Clinical Oncology 24(18_Suppl): 4570-4570
Nederhof, E.; van Oort, F.V.A.; Bouma, E.M.C.; Laceulle, O.M.; Oldehinkel, A.J.; Ormel, J. 2015: Predicting mental disorders from hypothalamic-pituitary-adrenal axis functioning: a 3-year follow-up in the TRAILS study. Psychological Medicine 45(11): 2403-2412
Timmerman, L.; Timman, R.; Laging, M.; Zuidema, W.C.; Beck, D.K.; IJzermans, J.N.M.; Busschbach, J.J.V.; Weimar, W.; Massey, E.K. 2016: Predicting mental health after living kidney donation: the importance of psychological factors. British Journal of Health Psychology 21(3): 533-554
De Müllenheim, P.-Y.; Dumond, R.ém.; Gernigon, M.; Mahé, G.; Lavenu, A.; Bickert, S.; Prioux, J.; Noury-Desvaux, B.én.éd.; Le Faucheur, A. 2016: Predicting metabolic rate during level and uphill outdoor walking using a low-cost GPS receiver. Journal of Applied Physiology 121(2): 577-588
Karimi-Alavijeh, F.; Jalili, S.; Sadeghi, M. 2016: Predicting metabolic syndrome using decision tree and support vector machine methods. Arya Atherosclerosis 12(3): 146-152
Couturier, J.; Trolet, J.; Hupe, P.; Desjardins, L.; Mariani, P.; Sastre-Garau, X.; Asselain, B.; Barillot, E.; Saule, S.; Piperno-Neumann, S. 2016: Predicting metastasis in uveal melanoma: Identification of a prognostic classifier by genomic profiling using array-CGH of 78 ocular tumors (OT) and 66 liver metastases (LM). Journal of Clinical Oncology 26(15_Suppl): 9041-9041
Zhang, W.; Le, T.Duy.; Liu, L.; Zhou, Z-Hua.; Li, J. 2016: Predicting miRNA Targets by Integrating Gene Regulatory Knowledge with Expression Profiles. Plos one 11(4): E0152860
Lan, W.; Wang, J.; Li, M.; Liu, J.; Wu, F-Xiang.; Pan, Y. 2018: Predicting MicroRNA-Disease Associations Based on Improved MicroRNA and Disease Similarities. Ieee/Acm Transactions on Computational Biology and Bioinformatics 15(6): 1774-1782
Li, C.; Lim, K.M.K.; Chng, K.R.; Nagarajan, N. 2016: Predicting microbial interactions through computational approaches. Methods 102: 12-19
Oliver, D.M.; Porter, K.D.H.; Pachepsky, Y.A.; Muirhead, R.W.; Reaney, S.M.; Coffey, R.; Kay, D.; Milledge, D.G.; Hong, E.; Anthony, S.G.; Page, T.; Bloodworth, J.W.; Mellander, P-Erik.; Carbonneau, P.E.; McGrane, S.J.; Quilliam, R.S. 2016: Predicting microbial water quality with models: Over-arching questions for managing risk in agricultural catchments. Science of the Total Environment 544: 39-47
Pardi, D.S. 2015: Predicting microscopic colitis before colon biopsies: a look into the crystal ball?. Clinical Gastroenterology and Hepatology: the Official Clinical Practice Journal of the American Gastroenterological Association 13(6): 1132-1133
Horobin, R.W. 2015: Predicting mitochondrial targeting by small molecule xenobiotics within living cells using QSAR models. Methods in Molecular Biology 1265: 13-23
Spaan, M.H.; Vrieling, A.H.; van de Berg, P.; Dijkstra, P.U.; van Keeken, H.G. 2017: Predicting mobility outcome in lower limb amputees with motor ability tests used in early rehabilitation. Prosthetics and Orthotics International 41(2): 171-177
Conti, S.; Cecchini, M. 2016: Predicting molecular self-assembly at surfaces: a statistical thermodynamics and modeling approach. Physical Chemistry Chemical Physics: Pccp 18(46): 31480-31493
Rosenberg, M.D.; Finn, E.S.; Constable, R.T.; Chun, M.M. 2015: Predicting moment-to-moment attentional state. Neuroimage 114: 249-256
Yadav, J.; Yadav, S.Kumar.; Kumar, S.; Baxla, R.George.; Sinha, D.Kumar.; Bodra, P.; Besra, R.Chandra.; Baski, B.Mani.; Prakash, O.; Anand, A. 2016: Predicting morbidity and mortality in acute pancreatitis in an Indian population: a comparative study of the BISAP score, Ranson's score and CT severity index. Gastroenterology Report 4(3): 216-220
Rothnie, K.J.; Smeeth, L.; Pearce, N.; Herrett, E.; Timmis, A.; Hemingway, H.; Wedzicha, J.; Quint, J.K. 2016: Predicting mortality after acute coronary syndromes in people with chronic obstructive pulmonary disease. Heart 102(18): 1442-1448
Wu, A.H.B.; Hale, K. 2015: Predicting mortality after elective open-heart surgery using midregional-proadrenomedullin: is it time to scalp Acute Physiology and Chronic Health Evaluation IV?. Critical Care Medicine 43(2): 494-495
Henderson, C.Y.; Ryan, J.P. 2015: Predicting mortality following hip fracture: an analysis of comorbidities and complications. Irish Journal of Medical Science 184(3): 667-671
Fraccaro, P.; Kontopantelis, E.; Sperrin, M.; Peek, N.; Mallen, C.; Urban, P.; Buchan, I.E.; Mamas, M.A. 2016: Predicting mortality from change-over-time in the Charlson Comorbidity Index: A retrospective cohort study in a data-intensive UK health system. Medicine 95(43): E4973
de Gelder, J.; Lucke, J.A.; Heim, N.; de Craen, A.J.M.; Lourens, S.D.; Steyerberg, E.W.; de Groot, B.; Fogteloo, A.J.; Blauw, G.J.; Mooijaart, S.P. 2016: Predicting mortality in acutely hospitalized older patients: a retrospective cohort study. Internal and Emergency Medicine 11(4): 587-594
Ellis, H.C.; Cowman, S.; Fernandes, M.; Wilson, R.; Loebinger, M.R. 2016: Predicting mortality in bronchiectasis using bronchiectasis severity index and FACED scores: a 19-year cohort study. European Respiratory Journal 47(2): 482-489
Ceniceros, A.; Pértega, S.; Galeiras, R.; Mourelo, M.ón.; López, E.; Broullón, J.; Sousa, D.; Freire, D. 2016: Predicting mortality in burn patients with bacteraemia. Infection 44(2): 215-222
Weiss, J.W.; Platt, R.W.; Thorp, M.L.; Yang, X.; Smith, D.H.; Petrik, A.; Eckstrom, E.; Morris, C.; O'Hare, A.M.; Johnson, E.S. 2015: Predicting mortality in older adults with kidney disease: a pragmatic prediction model. Journal of the American Geriatrics Society 63(3): 508-515
Rauh, S.P.; Heymans, M.W.; Mehr, D.R.; Kruse, R.L.; Lane, P.; Kowall, N.W.; Volicer, L.; van der Steen, J.T. 2016: Predicting mortality in patients treated differently: updating and external validation of a prediction model for nursing home residents with dementia and lower respiratory infections. BMJ open 6(8): e011380
Passantino, A.; Monitillo, F.; Iacoviello, M.; Scrutinio, D. 2015: Predicting mortality in patients with acute heart failure: Role of risk scores. World Journal of Cardiology 7(12): 902-911
George, E.C.; Walker, A.S.; Kiguli, S.; Olupot-Olupot, P.; Opoka, R.O.; Engoru, C.; Akech, S.O.; Nyeko, R.; Mtove, G.; Reyburn, H.; Berkley, J.A.; Mpoya, A.; Levin, M.; Crawley, J.; Gibb, D.M.; Maitland, K.; Babiker, A.G. 2015: Predicting mortality in sick African children: the FEAST Paediatric Emergency Triage (PET) Score. Bmc Medicine 13: 174
Lipshutz, A.K.M.; Feiner, J.R.; Grimes, B.; Gropper, M.A. 2016: Predicting mortality in the intensive care unit: a comparison of the University Health Consortium expected probability of mortality and the Mortality Prediction Model III. Journal of Intensive Care 4: 35
Goldstein, B.A.; Pencina, M.J.; Montez-Rath, M.E.; Winkelmayer, W.C. 2017: Predicting mortality over different time horizons: which data elements are needed?. Journal of the American Medical Informatics Association: Jamia 24(1): 176-181
Toua, R.Elaine.; de Kock, J.Erasmus.; Welzel, T. 2016: Predicting mortality rates: Comparison of an administrative predictive model (hospital standardized mortality ratio) with a physiological predictive model (Acute Physiology and Chronic Health Evaluation IV)--A cross-sectional study. Journal of Critical Care 31(1): 7-12
O'Shea, T.M. 2016: Predicting motor impairments among children born extremely preterm or very low birthweight. Developmental Medicine and Child Neurology 58(6): 531-532
Buch, E.R.; Rizk, S.; Nicolo, P.; Cohen, L.G.; Schnider, A.; Guggisberg, A.G. 2016: Predicting motor improvement after stroke with clinical assessment and diffusion tensor imaging. Neurology 86(20): 1924-1925
Shehee, L.; Coker-Bolt, P.; Barbour, A.; Moss, H.; Brown, T.; Jenkins, D. 2016: Predicting motor outcomes with 3 month prone hip angles in premature infants. Journal of Pediatric Rehabilitation Medicine 9(3): 231-236
Gim, J.; Cho, Y.B.; Hong, H.K.; Kim, H.C.; Yun, S.H.; Wu, H.-G.; Jeong, S.-Y.; Joung, J.-G.; Park, T.; Park, W.-Y.; Lee, W.Y. 2016: Predicting multi-class responses to preoperative chemoradiotherapy in rectal cancer patients. Radiation Oncology 11: 50
Sierra, M.; Grasa, J.; Muñoz, M.J.; Miana-Mena, F.J.; González, D. 2017: Predicting muscle fatigue: a response surface approximation based on proper generalized decomposition technique. Biomechanics and Modeling in Mechanobiology 16(2): 625-634
Edwards, C.; Tod, D.; Molnar, G.; Markland, D. 2016: Predicting muscularity-related behavior, emotions, and cognitions in men: the role of psychological need thwarting, drive for muscularity, and mesomorphic internalization. Body Image 18: 108-112
Norman, P.; Parello, J.; Polavarapu, P.L.; Linares, M. 2015: Predicting near-UV electronic circular dichroism in nucleosomal DNA by means of DFT response theory. Physical Chemistry Chemical Physics: Pccp 17(34): 21866-21879
Marke, V.; Bennett, P. 2017: Predicting negative emotional states following first onset acute coronary syndrome. Journal of Health Psychology 22(6): 765-775
DeMeo, S.D.; Katakam, L.; Goldberg, R.N.; Tanaka, D. 2015: Predicting neonatal intubation competency in trainees. Pediatrics 135(5): E1229-E1236
Wang, J.; Edginton, A.N.; Avant, D.; Burckart, G.J. 2015: Predicting neonatal pharmacokinetics from prior data using population pharmacokinetic modeling. Journal of Clinical Pharmacology 55(10): 1175-1183
Beamon, C.; Carlson, L.; Rambally, B.; Berchuck, S.; Gearhart, M.; Hammett-Stabler, C.; Strauss, R. 2016: Predicting neonatal respiratory morbidity by lamellar body count and gestational age. Journal of Perinatal Medicine 44(6): 677-683
Howe, T.-H.; Sheu, C.-F.; Hsu, Y.-W.; Wang, T.-N.; Wang, L.-W. 2016: Predicting neurodevelopmental outcomes at preschool age for children with very low birth weight. Research in Developmental Disabilities 48: 231-241
Kubo, Y.; Koji, T.; Yoshida, J.; Ogawa, A.; Ogasawara, K. 2016: Predicting neurological deficit severity due to subarachnoid haemorrhage: soluble CD40 ligand and platelet-derived growth factor-BB. Critical Care and Resuscitation: Journal of the Australasian Academy of Critical Care Medicine 18(4): 242-246
Cooper, M. 2012: Predicting neurological outcome. Emergency Nurse: the Journal of the Rcn Accident and Emergency Nursing Association 20(4): 11
Pawloski, P.A.; Thomas, A.J.; Kane, S.; Vazquez-Benitez, G.; Shapiro, G.R.; Lyman, G.H. 2017: Predicting neutropenia risk in patients with cancer using electronic data. Journal of the American Medical Informatics Association: Jamia 24(E1): E129-E135
Steen, S.T.; Chung, A.P.; Han, S.; Vinstein, A.; Yoon, J.L.; Giuliano, A.E. 2016: Predicting nipple-areolar involvement using preoperative breast MRI and primary tumor characteristics. Journal of Clinical Oncology 29(27_Suppl): 40-40
Reed, K.F.; Moraes, L.E.; Casper, D.P.; Kebreab, E. 2015: Predicting nitrogen excretion from cattle. Journal of Dairy Science 98(5): 3025-3035
Shellmer, D.A. 2015: Predicting non-adherence: Striking the right balance. Pediatric Transplantation 19(5): 449-451
Pallayova, M.; Mohammed, A.; Langman, G.; Taheri, S.; Dasgupta, I. 2015: Predicting non-diabetic renal disease in type 2 diabetic adults: the value of glycated hemoglobin. Journal of Diabetes and its Complications 29(5): 718-723
Rosellini, A.J.; Monahan, J.; Street, A.E.; Heeringa, S.G.; Hill, E.D.; Petukhova, M.; Reis, B.Y.; Sampson, N.A.; Bliese, P.; Schoenbaum, M.; Stein, M.B.; Ursano, R.J.; Kessler, R.C. 2016: Predicting non-familial major physical violent crime perpetration in the US Army from administrative data. Psychological Medicine 46(2): 303-316
Kohrt, H.E.; Olshen, R.A.; Goodson, W.H.; Rouse, R.V.; Bailey, L.; Philben, V.; Dirbas, F.M.; Stockdale, F.E.; Carlson, R.W.; Jeffrey, S.S. 2016: Predicting non-sentinel lymph node involvement in breast cancer patients. Journal of Clinical Oncology 24(18_Suppl): 531-531
Rydman, E.; Ponzer, S.; Ottosson, C.; Järnbert-Pettersson, H. 2017: Predicting nonrecovery among whiplash patients in the emergency room and in an insurance company setting. European Spine Journal: Official Publication of the European Spine Society the European Spinal Deformity Society and the European Section of the Cervical Spine Research Society 26(4): 1254-1261
Stec, M.; Spietz, T.; Więcław-Solny, L.; Tatarczuk, A.; Krótki, A. 2015: Predicting normal densities of amines using quantitative structure-property relationship (QSPR). Sar and Qsar in Environmental Research 26(11): 893-904
Dachet, F.; Bagla, S.; Keren-Aviram, G.; Morton, A.; Balan, K.; Saadat, L.; Valyi-Nagy, T.; Kupsky, W.; Song, F.; Dratz, E.; Loeb, J.A. 2015: Predicting novel histopathological microlesions in human epileptic brain through transcriptional clustering. Brain: a Journal of Neurology 138(Part 2): 356-370
Goodacre, N.; Edwards, N.; Danielsen, M.; Uetz, P.; Wu, C. 2017: Predicting ns SNPs that Disrupt Protein-Protein Interactions Using Docking. Ieee/Acm Transactions on Computational Biology and Bioinformatics 14(5): 1082-1093
Xie, Y.; Schreier, G.ün.; Chang, D.C.W.; Neubauer, S.; Redmond, S.J.; Lovell, N.H. 2014: Predicting number of hospitalization days based on health insurance claims data using bagged regression trees. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2014: 2706-2709
Whittaker, J.B.; Tribe, N.P. 1998: Predicting numbers of an insect (Neophilaenus lineatus: Homoptera) in a changing climate. Journal of Animal Ecology 67(6): 987-991
Simpson, K.R. 2015: Predicting nurse staffing needs for a labor and birth unit in a large-volume perinatal service. Journal of Obstetric Gynecologic and Neonatal Nursing: Jognn 44(2): 329-338
Strudwick, G. 2015: Predicting nurses' use of healthcare technology using the technology acceptance model: an integrative review. Computers Informatics Nursing: Cin 33(5): 189-198; Quiz E1
Greiner, M.A.; Qualls, L.G.; Iwata, I.; White, H.K.; Molony, S.L.; Sullivan, M.Terry.; Burke, B.; Schulman, K.A.; Setoguchi, S. 2014: Predicting nursing home placement among home- and community-based services program participants. American Journal of Managed Care 20(12): E535-E536
Vanni, M.J.; McIntyre, P.B. 2016: Predicting nutrient excretion of aquatic animals with metabolic ecology and ecological stoichiometry: a global synthesis. Ecology 97(12): 3460-3471
Ansuini, C.; Cavallo, A.; Koul, A.; Jacono, M.; Yang, Y.; Becchio, C. 2015: Predicting object size from hand kinematics: a temporal perspective. Plos one 10(3): E0120432
Besharat, M.Ali.; Kamali, Z.Sadat. 2016: Predicting obsessions and compulsions according to superego and ego characteristics: A comparison between scrupulosity and non-religious obsessive-compulsive symptoms. Asian Journal of Psychiatry 19: 73-78
Meister, M.R.L.; Cahill, A.G.; Conner, S.N.; Woolfolk, C.L.; Lowder, J.L. 2016: Predicting obstetric anal sphincter injuries in a modern obstetric population. American Journal of Obstetrics and Gynecology 215(3): 310.E1-7
Tan, A.; Yin, J.D.C.; Tan, L.W.L.; van Dam, R.M.; Cheung, Y.Yi.; Lee, C-Hang. 2016: Predicting obstructive sleep apnea using the STOP-Bang questionnaire in the general population. Sleep Medicine 27-28: 66-71
Zargar, H.; Zargar-Shoshtari, K.; Dundee, P.; Black, P.C. 2016: Predicting occult lymph node-positive disease at the time of radical cystectomy: a systematic review. Minerva Urologica e Nefrologica 68(2): 112-124
Yamane, S.; Nambu, I.; Wada, Y. 2014: Predicting occurrence of errors during a Go/No-Go task from EEG signals using Support Vector Machine. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2014: 4944-4947
Oh, B.Z.L.; Sequeira, A.M.M.; Meekan, M.G.; Ruppert, J.L.W.; Meeuwig, J.J. 2017: Predicting occurrence of juvenile shark habitat to improve conservation planning. Conservation Biology: the Journal of the Society for Conservation Biology 31(3): 635-645
Dexter, F. 2017: Predicting odds of prolonged operative times. American Journal of Surgery 213(1): 202
Zendehdel, R.; Khodakarim, S.; H Shirazi, F. 2015: Predicting of Effective Dose as Biomarker for Cytotoxicity Using Partial Least Square-Fourier Transform Infrared Spectroscopy (PLS_FTIR). Iranian Journal of Pharmaceutical Research: Ijpr 14(4): 1189-1196
Qu, H.; Li, Z.; Zhai, Z.; Liu, C.; Wang, S.; Guo, S.; Zhang, Z. 2015: Predicting of Venous Thromboembolism for Patients Undergoing Gynecological Surgery. Medicine 94(39): E1653
Zhang, Q.; He, M.; Wang, J.; Liu, S.; Cheng, H.; Cheng, Y. 2015: Predicting of disease genes for gestational diabetes mellitus based on network and functional consistency. European Journal of Obstetrics Gynecology and Reproductive Biology 186: 91-96
Genov, P.G.; Smirnova, O.V.; Glushchenko, N.S.; Timerbaev, V.H.; Rebrova, O.J. 2015: Predicting of postoperative pain level and morphine consumption by preoperative pressure pain assessment in patients before elective surgery. Anesteziologiia i Reanimatologiia 60(1): 11-16
Devyatova, E.A.; Tsaturova, K.A.; Vartanyan, E.V. 2016: Predicting of successful implantation at IVF cycles. Gynecological Endocrinology: the Official Journal of the International Society of Gynecological Endocrinology 32(Sup2): 27-29
Ivanov, S.D.; Korytova, L.L. 2015: Predicting of the effectiveness of radiotherapy and chemoradiotherapy for cancer patients. Voprosy Onkologii 61(6): 876-888
Suffoletto, B.; Miller, T.; Shah, R.; Callaway, C.; Yealy, D.M. 2016: Predicting older adults who return to the hospital or die within 30 days of emergency department care using the ISAR tool: subjective versus objective risk factors. Emergency Medicine Journal: Emj 33(1): 4-9
Jones Ross, R.W.; Cordazzo, S.T.D.; Scialfa, C.T. 2014: Predicting on-road driving performance and safety in healthy older adults. Journal of Safety Research 51: 73-80
Cao, X.-Y.; Zhou, J.-H.; Cai, G.-Y.; Tan, N.-N.; Huang, J.; Xie, X.-C.; Tang, L.; Chen, X.-M. 2015: Predicting one-year mortality in peritoneal dialysis patients: an analysis of the China Peritoneal Dialysis Registry. International Journal of Medical Sciences 12(4): 354-361
Poukkanen, M.; Vaara, S.T.; Reinikainen, M.; Selander, T.; Nisula, S.; Karlsson, S.; Parviainen, I.; Koskenkari, J.; Pettilä, V.; Laru-Sompa, R.; Pulkkinen, A.; Saarelainen, M.; Reilama, M.; Tolmunen, S.; Rantalainen, U.; Miettinen, M.; Suvela, M.; Pesola, K.; Saastamoinen, P.; Kauppinen, S.; Pettilä, V.; Kaukonen, K.-M.; Korhonen, A.-M.; Nisula, S.; Vaara, S.; Suojaranta-Ylinen, R.; Mildh, L.; Haapio, M.; Nurminen, L.; Sutinen, S.; Pettilä, L.; Laitinen, H.ä; Syrjä, H.; Henttonen, K.; Lappi, E.; Boman, H.; Varpula, T.; Porkka, P.äi.; Sivula, M.; Rahkonen, M.; Tsurkka, A.; Nieminen, T.; Prittinen, N.; Alaspää, A.; Salanto, V.; Juntunen, H.; Sanisalo, T.; Parviainen, I.; Uusaro, A.; Ruokonen, E.; Bendel, S.; Rissanen, N.; Lång, M.;, 2015: Predicting one-year mortality of critically ill patients with early acute kidney injury: data from the prospective multicenter FINNAKi study. Critical Care 19: 125
Grounds, R.; Al-Hussaini, A.; Owens, D. 2015: Predicting operative duration and implications for list planning: a retrospective analysis of data from 85 adults and 72 children undergoing tonsillectomy: Our Experience. Clinical Otolaryngology: Official Journal of Ent-Uk ; Official Journal of Netherlands Society for Oto-Rhino-Laryngology and Cervico-Facial Surgery 40(5): 483-486
Keller, D.S.; Parikh, N.; Senagore, A.J. 2017: Predicting opportunities to increase utilization of laparoscopy for colon cancer. Surgical Endoscopy 31(4): 1855-1862
Greischar, M.A.; Mideo, N.; Read, A.F.; Bjørnstad, O.N. 2016: Predicting optimal transmission investment in malaria parasites. Evolution; International Journal of Organic Evolution 70(7): 1542-1558
Kuhs, M.; Moore, J.; Kollamaram, G.; Walker, G.; Croker, D. 2017: Predicting optimal wet granulation parameters for extrusion-spheronisation of pharmaceutical pellets using a mixer torque rheometer. International Journal of Pharmaceutics 517(1-2): 19-24
Diamond, G.L.; Bradham, K.D.; Brattin, W.J.; Burgess, M.; Griffin, S.; Hawkins, C.A.; Juhasz, A.L.; Klotzbach, J.M.; Nelson, C.; Lowney, Y.W.; Scheckel, K.G.; Thomas, D.J. 2016: Predicting oral relative bioavailability of arsenic in soil from in vitro bioaccessibility. Journal of Toxicology and Environmental Health. Part a 79(4): 165-173
Taghavi Bayat, J.; Huggare, J.; Mohlin, B.; Akrami, N. 2017: Predicting orthodontic treatment need: reliability and validity of the Demand for Orthodontic Treatment Questionnaire. European Journal of Orthodontics 39(3): 326-333
Werner, R.A. 1994: Predicting outcome after acute stroke with the Functional Independence Measure. Topics in Stroke Rehabilitation 1(3): 30-39
Separham, A.; Pourafkari, L.; Bodagh, H.; Ghaffari, S.; Aslanabadi, N.; Nader, N.D. 2017: Predicting outcome after percutaneous balloon mitral commissurotomy : Role of neutrophil-lymphocyte ratio. Herz 42(5): 509-514
Maas, A.I.R.; Lingsma, H.F.; Roozenbeek, B. 2015: Predicting outcome after traumatic brain injury. Handbook of Clinical Neurology 128: 455-474
Moaddab, A.; Tonni, G.; Grisolia, G.; Bonasoni, M.Paola.; Araujo Júnior, E.; Rolo, L.Cristine.; Prefumo, F.; de la Fuente, S.; Sepulveda, W.; Ayres, N.; Ruano, R. 2016: Predicting outcome in 259 fetuses with agenesis of ductus venosus - a multicenter experience and systematic review of the literature (.). Journal of Maternal-Fetal and Neonatal Medicine: the Official Journal of the European Association of Perinatal Medicine the Federation of Asia and Oceania Perinatal Societies the International Society of Perinatal Obstetricians 29(22): 3606-3614
Pourafkari, L.; Ghaffari, S.; Afshar, A.H.; Anwar, S.; Nader, N.D. 2015: Predicting outcome in acute heart failure, does it matter?. Acta Cardiologica 70(6): 653-663
Bihari, S.; Bersten, A.D. 2015: Predicting outcome in acute respiratory distress syndrome-putting some science behind crystal gazing*. Critical Care Medicine 43(2): 481-482
Lynch, R.W.; Churchhouse, A.M.D.; Protheroe, A.; Arnott, I.D.R. 2016: Predicting outcome in acute severe ulcerative colitis: comparison of the Travis and Ho scores using UK IBD audit data. Alimentary Pharmacology and Therapeutics 43(11): 1132-1141
Wottschel, V.; Alexander, D.C.; Kwok, P.P.; Chard, D.T.; Stromillo, M.L.; De Stefano, N.; Thompson, A.J.; Miller, D.H.; Ciccarelli, O. 2015: Predicting outcome in clinically isolated syndrome using machine learning. Neuroimage. Clinical 7: 281-287
Jackson, T.A.; Wilson, D.; Richardson, S.; Lord, J.M. 2016: Predicting outcome in older hospital patients with delirium: a systematic literature review. International Journal of Geriatric Psychiatry 31(4): 392-399
Casey, D.L.; Wexler, L.H.; Fox, J.J.; Dharmarajan, K.V.; Schoder, H.; Price, A.N.; Wolden, S.L. 2014: Predicting outcome in patients with rhabdomyosarcoma: role of [(18)f]fluorodeoxyglucose positron emission tomography. International Journal of Radiation Oncology Biology Physics 90(5): 1136-1142
Tjepkema-Cloostermans, M.C.; Hofmeijer, J.; Hom, H.W.; Bosch, F.H.; van Putten, M.J.A.M. 2017: Predicting Outcome in Postanoxic Coma: Are Ten EEG Electrodes Enough?. Journal of Clinical Neurophysiology: Official Publication of the American Electroencephalographic Society 34(3): 207-212
Nakache, G.; Primov-Fever, A.; Alon, E.E.; Wolf, M. 2015: Predicting outcome in tracheal and cricotracheal segmental resection. European Archives of Oto-Rhino-Laryngology: Official Journal of the European Federation of Oto-Rhino-Laryngological Societies: Affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery 272(6): 1471-1475
Pal, R.; Munivenkatappa, A.; Agrawal, A.; Menon, G.R.; Galwankar, S.; Mohan, P.R.; Kumar, S.S.; Subrahmanyam, B.V. 2016: Predicting outcome in traumatic brain injury: Sharing experience of pilot traumatic brain injury registry. International Journal of Critical Illness and Injury Science 6(3): 127-132
Quan, L.; Bierens, J.J.L.M.; Lis, R.; Rowhani-Rahbar, A.; Morley, P.; Perkins, G.D. 2016: Predicting outcome of drowning at the scene: a systematic review and meta-analyses. Resuscitation 104: 63-75
Wirsching, H-Georg.; Morel, C.; Gmür, C.; Neidert, M.Christoph.; Baumann, C.Richard.; Valavanis, A.; Rushing, E.Jane.; Krayenbühl, N.; Weller, M. 2016: Predicting outcome of epilepsy after meningioma resection. Neuro-Oncology 18(7): 1002-1010
Prebet, T.; Fenaux, P.; Vey, N. 2016: Predicting outcome of patients with myelodysplastic syndromes after failure of azacitidine: validation of the North American MDS consortium scoring system. Haematologica 101(10): e427-e428
Leitinger, M.; Kalss, G.; Rohracher, A.; Pilz, G.; Novak, H.; Höfler, J.; Deak, I.; Kuchukhidze, G.; Dobesberger, J.; Wakonig, A.; Trinka, E. 2015: Predicting outcome of status epilepticus. Epilepsy and Behavior: E&b 49: 126-130
Crits-Christoph, P.; Markell, H.M.; Gallop, R.; Gibbons, M.Beth.Connolly.; McClure, B.; Rotrosen, J. 2015: Predicting outcome of substance abuse treatment in a feedback study: Can recovery curves be improved upon?. PsychoTherapy Research: Journal of the Society for PsychoTherapy Research 25(6): 694-704
Speiser, J.L.; Lee, W.M.; Karvellas, C.J.; Lee, W.M.; Larson, A.M.; Liou, I.; Davern, T.; Fix, O.; Schilsky, M.; McCashland, T.; Hay, J. E.; Murray, N.; Shaikh, A. O.S.; Blei, A.; Ganger, D.; Zaman, A.; Han, S.H.B.; Fontana, R.; McGuire, B.; Chung, R.T.; Smith, A.; Brown, R.; Crippin, J.; Harrison, E.; Reuben, A.; Munoz, S.; Reddy, R.; Stravitz, R. T.; Rossaro, L.; Satyanarayana, R.; Hassanein, T.; Hanje, J.; Olson, J.; Subramanian, R.; Karvellas, C.J.; Samuel, G.; Lalani, E.; Pezzia, C.; Sanders, C.; Attar, N.; Hynan, L.S.; Durkalski, V.; Zhao, W.; Speiser, J.; Dillon, C.; Battenhouse, H.; Gottfried, M. 2015: Predicting outcome on admission and post-admission for acetaminophen-induced acute liver failure using classification and regression tree models. Plos one 10(4): E0122929
Barros-Gomes, S.; Eleid, M.F.; Dahl, J.S.; Pislaru, C.; Nishimura, R.A.; Pellikka, P.A.; Pislaru, S.V. 2017: Predicting outcomes after percutaneous mitral balloon valvotomy: the impact of left ventricular strain imaging. European Heart Journal. Cardiovascular Imaging 18(7): 763-771
Kim, E.-J.; Ozonoff, A.; Hylek, E.M.; Berlowitz, D.R.; Ash, A.S.; Miller, D.R.; Zhao, S.; Reisman, J.I.; Jasuja, G.K.; Rose, A.J. 2015: Predicting outcomes among patients with atrial fibrillation and heart failure receiving anticoagulation with warfarin. Thrombosis and Haemostasis 114(1): 70-77
Normand, C.; Dickstein, K. 2015: Predicting outcomes following CRT: the quest continues. European Journal of Heart Failure 17(7): 645-646
Venkataratamani, P.V.; Murthy, A. 2018: Distinct mechanisms explain the control of reach speed planning: evidence from a race model framework. Journal of Neurophysiology 120(3): 1293-1306
Ding, D. 2015: Predicting outcomes from radiosurgery for intracranial arteriovenous malformations: effect of embolization, prior hemorrhage, and nidus anatomy. Neurological Sciences: Official Journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology 36(6): 1025-1026
Karki, P.; Agrawaal, K.K.; Lamsal, M.; Shrestha, N.R. 2015: Predicting outcomes in acute coronary syndrome using biochemical markers. Indian Heart Journal 67(6): 529-537
Ventham, N.T.; Kalla, R.; Kennedy, N.A.; Satsangi, J.; Arnott, I.D. 2015: Predicting outcomes in acute severe ulcerative colitis. Expert Review of Gastroenterology and Hepatology 9(4): 405-415
Søreide, K.; Thorsen, K.; Søreide, J.A. 2015: Predicting outcomes in patients with perforated gastroduodenal ulcers: artificial neural network modelling indicates a highly complex disease. European Journal of Trauma and Emergency Surgery: Official Publication of the European Trauma Society 41(1): 91-98
Avelino, M.; Maunsell, R.; Jubé Wastowski, I. 2015: Predicting outcomes of balloon laryngoplasty in children with subglottic stenosis. International Journal of Pediatric Otorhinolaryngology 79(4): 532-536
Waqas, M.; Shamim, M.S.; Enam, S.F.; Qadeer, M.; Bakhshi, S.K.; Patoli, I.; Ahmad, K. 2016: Predicting outcomes of decompressive craniectomy: use of Rotterdam Computed Tomography Classification and Marshall Classification. British Journal of Neurosurgery 30(2): 258-263
Jung, E.Y.; Park, K.H.; Lee, S.Y.; Ryu, A.; Joo, J.K.; Park, J.W. 2016: Predicting outcomes of emergency cerclage in women with cervical insufficiency using inflammatory markers in maternal blood and amniotic fluid. International Journal of Gynaecology and Obstetrics: the Official Organ of the International Federation of Gynaecology and Obstetrics 132(2): 165-169
Ashraf, M.; Souka, A.; Adelman, R. 2016: Predicting outcomes to anti-vascular endothelial growth factor (VEGF) therapy in diabetic macular oedema: a review of the literature. British Journal of Ophthalmology 100(12): 1596-1604
Wijeysundera, D.N. 2016: Predicting outcomes: Is there utility in risk scores?. Canadian Journal of Anaesthesia 63(2): 148-158
Duan, Z.; Hansen, T.H.; Hansen, T.B.; Dalgaard, P.; Knøchel, S. 2016: Predicting outgrowth and inactivation of Clostridium perfringens in meat products during low temperature long time heat treatment. International Journal of Food Microbiology 230: 45-57
Dimitrakopoulos, C.; Theofilatos, K.; Pegkas, A.; Likothanassis, S.; Mavroudi, S. 2016: Predicting overlapping protein complexes from weighted protein interaction graphs by gradually expanding dense neighborhoods. Artificial Intelligence in Medicine 71: 62-69
Griffiths, M.Z.; Alkorta, I.; Popelier, P.L.A. 2013: Predicting pKa Values in Aqueous Solution for the Guanidine Functional Group from Gas Phase Ab Initio Bond Lengths. Molecular Informatics 32(4): 363-376
Dušek, T.; Ferko, A.; Orhalmi, J.; Chobola, M.; Sotona, O.; Hadži Nikolov, D.; Hovorková, E. 2014: Predicting pN positivity in T3 rectal cancer. Rozhledy V Chirurgii: Mesicnik Ceskoslovenske Chirurgicke Spolecnosti 93(12): 572-576
Nieuweboer, A.J.M.; Smid, M.; de Graan, A.-J.M.; Elbouazzaoui, S.; de Bruijn, P.; Martens, J.W.; Mathijssen, R.H.J.; van Schaik, R.H.N. 2015: Predicting paclitaxel-induced neutropenia using the DMET platform. Pharmacogenomics 16(11): 1231-1241
Rosenbloom, B.N.; Katz, J.; Chin, K.Y.W.; Haslam, L.; Canzian, S.; Kreder, H.J.; McCartney, C.J.L. 2016: Predicting pain outcomes after traumatic musculoskeletal injury. Pain 157(8): 1733-1743
Krishnaswamy, A.; Tuzcu, E.M. 2015: Predicting paravalvular leak after balloon-expandable TAVR. Catheterization and Cardiovascular Interventions: Official Journal of the Society for Cardiac Angiography and Interventions 86(1): 152-153
Hiemstra-van Mastrigt, S.; Groenesteijn, L.; Vink, P.; Kuijt-Evers, L.F.M. 2017: Predicting passenger seat comfort and discomfort on the basis of human, context and seat characteristics: a literature review. Ergonomics 60(7): 889-911
Crauste, F.; Terry, E.; Mercier, I.L.; Mafille, J.; Djebali, S.; Andrieu, T.; Mercier, B.; Kaneko, G.; Arpin, C.; Marvel, J.; Gandrillon, O. 2015: Predicting pathogen-specific CD8 T cell immune responses from a modeling approach. Journal of Theoretical Biology 374: 66-82
Groheux, D. 2014: Predicting pathological complete response in breast cancer early. LANCET. Oncology 15(13): 1415-1416
Ryan, J.E.; Warrier, S.K.; Lynch, A.C.; Ramsay, R.G.; Phillips, W.A.; Heriot, A.G. 2016: Predicting pathological complete response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer: a systematic review. Colorectal Disease: the Official Journal of the Association of Coloproctology of Great Britain and Ireland 18(3): 234-246
Vederhus, J.-K.år.; Zemore, S.E.; Rise, J.; Clausen, T.; Høie, M. 2015: Predicting patient post-detoxification engagement in 12-step groups with an extended version of the theory of planned behavior. Addiction Science and Clinical Practice 10: 15
Li, B.; Cairns, J.A.; Robb, M.L.; Johnson, R.J.; Watson, C.J.E.; Forsythe, J.L.; Oniscu, G.C.; Ravanan, R.; Dudley, C.; Roderick, P.; Metcalfe, W.; Tomson, C.R.; Bradley, J.A. 2016: Predicting patient survival after deceased donor kidney transplantation using flexible parametric modelling. Bmc Nephrology 17(1): 51
Quaglino, P.; Matthiessen, L.W.; Curatolo, P.; Muir, T.; Bertino, G.; Kunte, C.; Odili, J.; Rotunno, R.; Humphreys, A.C.; Letulé, V.; Marenco, F.; Cuthbert, C.; Albret, R.; Benazzo, M.; De Terlizzi, F.; Gehl, J. 2015: Predicting patients at risk for pain associated with electrochemotherapy. Acta Oncologica 54(3): 298-306
Schoell, S.L.; Doud, A.N.; Weaver, A.A.; Barnard, R.T.; Meredith, J.W.; Stitzel, J.D.; Martin, R.S. 2015: Predicting patients that require care at a trauma center: analysis of injuries and other factors. Injury 46(4): 558-563
Koshy, M.; Battafarano, R.; Burrows, W.; Krasna, M.; Greenwald, B.; Mannuel, H.; Suntharalingam, M. 2016: Predicting patterns of failure in esophageal cancer following tri-modality therapy: Why histology matters. Journal of Clinical Oncology 26(15_Suppl): 4553-4553
Bertram, J.; Gomez, K.; Masel, J. 2017: Predicting patterns of long-term adaptation and extinction with population genetics. Evolution; International Journal of Organic Evolution 71(2): 204-214
Totosy de Zepetnek, J.O.; Au, J.S.; Hol, A.T.; Eng, J.J.; MacDonald, M.J. 2016: Predicting peak oxygen uptake from submaximal exercise after spinal cord injury. Applied Physiology Nutrition and Metabolism 41(7): 775-781
Indraccolo, U.; Scutiero, G.; Greco, P. 2016: Predicting pelvic visceral hypersensitivity from the discomfort of Lugol' test during colposcopy. European Review for Medical and Pharmacological Sciences 20(13): 2762-2763
Bello, M.; Campos-Rodriguez, R.; Rojas-Hernandez, S.; Contis-Montes de Oca, A.; Correa-Basurto, J.é 2015: Predicting peptide vaccine candidates against H1N1 influenza virus through theoretical approaches. Immunologic Research 62(1): 3-15
Chen, T.S.; Petrey, D.; Garzon, J.I.; Honig, B. 2015: Predicting peptide-mediated interactions on a genome-wide scale. Plos Computational Biology 11(5): E1004248
George, N.R.; Steffen, A.M. 2017: Predicting perceived medication-related hassles in dementia family caregivers. Dementia 16(6): 797-810
Oexle, N.; Ajdacic-Gross, V.; Müller, M.; Rodgers, S.; Rössler, W.; Rüsch, N. 2015: Predicting perceived need for mental health care in a community sample: an application of the self-regulatory model. Social Psychiatry and Psychiatric Epidemiology 50(10): 1593-1600
Cameron, E.; French, D.P. 2016: Predicting perceived safety to drive the morning after drinking: the importance of hangover symptoms. Drug and Alcohol Review 35(4): 442-446
Wang, F.; Huang, J.; Lv, Y.; Ma, X.; Yang, B.; Wang, E.; Du, B.; Li, W.; Song, Y. 2016: Predicting perceptual learning from higher-order cortical processing. Neuroimage 124(Part A): 682-692
Radicchi, F. 2015: Predicting percolation thresholds in networks. Physical Review. e Statistical Nonlinear and Soft Matter Physics 91(1): 010801
Dippong, J.; Kalkhoff, W. 2015: Predicting performance expectations from affective impressions: linking affect control theory and status characteristics theory. Social Science Research 50: 1-14
Stegers-Jager, K.M.; Themmen, A.P.N.; Cohen-Schotanus, J.; Steyerberg, E.W. 2015: Predicting performance: relative importance of students' background and past performance. Medical Education 49(9): 933-945
Rahbari, A.; Montazerian, H.; Davoodi, E.; Homayoonfar, S. 2017: Predicting permeability of regular tissue engineering scaffolds: scaling analysis of pore architecture, scaffold length, and fluid flow rate effects. Computer Methods in Biomechanics and Biomedical Engineering 20(3): 231-241
Manganaro, A.; Pizzo, F.; Lombardo, A.; Pogliaghi, A.; Benfenati, E. 2016: Predicting persistence in the sediment compartment with a new automatic software based on the k-Nearest Neighbor (k-NN) algorithm. Chemosphere 144: 1624-1630
Rohde, P.; Stice, E.; Gau, J.M. 2017: Predicting persistence of eating disorder compensatory weight control behaviors. International Journal of Eating Disorders 50(5): 561-568
Bushnell, G.A.; Stürmer, T.; White, A.; Pate, V.; Swanson, S.A.; Azrael, D.; Miller, M. 2016: Predicting persistence to antidepressant treatment in administrative claims data: Considering the influence of refill delays and prior persistence on other medications. Journal of Affective Disorders 196: 138-147
Fan, H.; Zhu, Q.; Ma, G.; Shen, C.; Zhang, B.; Wang, W. 2016: Predicting personality disorder functioning styles by the Chinese Adjective Descriptors of Personality: a preliminary trial in healthy people and personality disorder patients. Bmc Psychiatry 16: 302
Chen, H.; Grieneisen, M.L.; Zhang, M. 2016: Predicting pesticide removal efficacy of vegetated filter strips: a meta-regression analysis. Science of the Total Environment 548-549: 122-130
Haraya, K.; Tachibana, T.; Nezu, J. 2017: Predicting pharmacokinetic profile of therapeutic antibodies after iv injection from only the data after sc injection in cynomolgus monkey. Xenobiotica; the Fate of Foreign Compounds in Biological Systems 47(3): 194-201
Yu, M.-M.; Gao, Z.-W.; Chen, X.-Y.; Zhong, D.-F. 2014: Predicting pharmacokinetics of anti-cancer drug, famitinib in human using physiologically based pharmacokinetic model. Yao Xue Xue Bao 49(12): 1684-1688
Nightingale, T.E.; Walhin, J.P.; Thompson, D.; Bilzon, J.L.J. 2015: Predicting physical activity energy expenditure in wheelchair users with a multisensor device. Bmj Open Sport and Exercise Medicine 1(1)
Drain, J.; Billing, D.; Neesham-Smith, D.; Aisbett, B. 2016: Predicting physiological capacity of human load carriage - a review. Applied Ergonomics 52: 85-94
Jain, S.; Bader, G.D. 2016: Predicting physiologically relevant SH3 domain mediated protein-protein interactions in yeast. Bioinformatics 32(12): 1865-1872
Nakonezny, P.A.; Mayes, T.L.; Byerly, M.J.; Emslie, G.J. 2015: Predicting placebo response in adolescents with major depressive disorder: the Adolescent Placebo Impact Composite Score (APICS). Journal of Psychiatric Research 68: 346-353
Flora, P.K.; Brawley, L.R.; Sessford, J.D.; Cary, M.A.; Gyurcsik, N.C. 2016: Predicting planned physical activity of individuals with arthritis: a self-regulatory perspective. Journal of Health Psychology 21(11): 2684-2694
Bahlai, C.A.; Landis, D.A. 2016: Predicting plant attractiveness to pollinators with passive crowdsourcing. Royal Society Open Science 3(6): 150677
Brown, K.A.; Parks, K.E.; Bethell, C.A.; Johnson, S.E.; Mulligan, M. 2015: Predicting plant diversity patterns in Madagascar: understanding the effects of climate and land cover change in a biodiversity hotspot. Plos one 10(4): E0122721
Kader, M.; Lamb, D.T.; Mahbub, K.R.; Megharaj, M.; Naidu, R. 2016: Predicting plant uptake and toxicity of lead (Pb) in long-term contaminated soils from derived transfer functions. Environmental Science and Pollution Research International 23(15): 15460-15470
Lamb, D.T.; Kader, M.; Ming, H.; Wang, L.; Abbasi, S.; Megharaj, M.; Naidu, R. 2016: Predicting plant uptake of cadmium: validated with long-term contaminated soils. Ecotoxicology 25(8): 1563-1574
Skelton, R.P.; West, A.G.; Dawson, T.E. 2015: Predicting plant vulnerability to drought in biodiverse regions using functional traits. Proceedings of the National Academy of Sciences of the United States of America 112(18): 5744-5749
James, L.S.; Sakata, J.T. 2015: Predicting plasticity: acute context-dependent changes to vocal performance predict long-term age-dependent changes. Journal of Neurophysiology 114(4): 2328-2339
Coyle, W.L.; Guillemain, P.; Kergomard, J.; Dalmont, J.-P. 2015: Predicting playing frequencies for clarinets: a comparison between numerical simulations and simplified analytical formulas. Journal of the Acoustical Society of America 138(5): 2770-2781
Tanaka, T.; Shinya, T.; Sato, S.; Mitsuhashi, T.; Ichimura, K.; Soh, J.; Toyooka, S.; Kaji, M.; Miyoshi, S.; Kanazawa, S. 2015: Predicting pleural invasion using HRCT and 18F-FDG PET/CT in lung adenocarcinoma with pleural contact. Annals of Nuclear Medicine 29(9): 757-765
Abdullah, R.; Alhusainy, W.; Woutersen, J.; Rietjens, I.M.C.M.; Punt, A. 2016: Predicting points of departure for risk assessment based on in vitro cytotoxicity data and physiologically based kinetic (PBK) modeling: the case of kidney toxicity induced by aristolochic acid i. Food and Chemical Toxicology: An International Journal Published for the British Industrial Biological Research Association 92: 104-116
Wasserman, J.C.; Wasserman, M.A.él.V.; Barrocas, P.R.G.; Almeida, A.M. 2016: Predicting pollutant concentrations in the water column during dredging operations: Implications for sediment quality criteria. Marine Pollution Bulletin 108(1-2): 24-32
Musto, P.; Simeon, V.; Grossi, A.; Gay, F.; Bringhen, S.; Larocca, A.; Guariglia, R.; Pietrantuono, G.; Villani, O.; D'Arena, G.; Cuomo, C.; Musto, C.; Morabito, F.; Petrucci, M.T.; Offidani, M.; Zamagni, E.; Tacchetti, P.; Conticello, C.; Milone, G.; Palumbo, A.; Cavo, M.; Boccadoro, M. 2015: Predicting poor peripheral blood stem cell collection in patients with multiple myeloma receiving pre-transplant induction therapy with novel agents and mobilized with cyclophosphamide plus granulocyte-colony stimulating factor: results from a Gruppo Italiano Malattie EMatologiche dell'Adulto Multiple Myeloma Working Party study. Stem Cell Research and Therapy 6: 64
Brockmann, P.E.; Schlaud, M.; Poets, C.F.; Urschitz, M.S. 2015: Predicting poor school performance in children suspected for sleep-disordered breathing. Sleep Medicine 16(9): 1077-1083
Wilson, K.B.; Overholt, M.F.; Hogan, E.K.; Schwab, C.; Shull, C.M.; Ellis, M.; Grohmann, N.S.; Dilger, A.C.; Boler, D.D. 2016: Predicting pork loin chop yield using carcass and loin characteristics. Journal of Animal Science 94(11): 4903-4910
Balage, J.M.; da Luz E Silva, S.; Gomide, C.A.; Bonin, M.d.N.; Figueira, A.C. 2015: Predicting pork quality using Vis/NIR spectroscopy. Meat Science 108: 37-43
Saragih Turnip, S.; Sörbom, D.; Hauff, E. 2016: Predicting positive mental health in internally displaced persons in Indonesia: the roles of economic improvement and exposure to violent conflict. Psychology Health and Medicine 21(3): 286-294
Yong, W.S.; Chang, M.H.; Chen, W.J.J. 2016: Predicting possibility of positive non-sentinel nodes in sentinel node-positive breast cancer patients: A Singapore series. Journal of Clinical Oncology 29(27_Suppl): 51-51
Papachristos, A.; Howard, T.; Thomson, B.N.; Thomas, P.R. 2018: Predicting post-endoscopic retrograde cholangiopancreatography pancreatitis using the 4-h serum lipase level. ANZ Journal of Surgery 88(1-2): 82-86
Haagen, J.F.G.; Ter Heide, F.J.J.; Mooren, T.M.; Knipscheer, J.W.; Kleber, R.J. 2017: Predicting post-traumatic stress disorder treatment response in refugees: Multilevel analysis. British Journal of Clinical Psychology 56(1): 69-83
Huddar, V.; Rajan, V.; Bhattacharya, S.; Roy, S. 2014: Predicting postoperative acute respiratory failure in critical care using nursing notes and physiological signals. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2014: 2702-2705
Kashani, R.G.; Sareh, S.; Genovese, B.; Hershey, C.; Rezentes, C.; Shemin, R.; Buch, E.; Benharash, P. 2015: Predicting postoperative atrial fibrillation using CHA2DS2-VASc scores. Journal of Surgical Research 198(2): 267-272
Chan, M.T.V.; Gin, T. 2015: Predicting postoperative cardiac complications using automated endothelial function test. Hong Kong Medical Journal 21(Suppl 6): 17-18
Chen, Y.; Cao, W.; Gao, X.; Ong, H.; Ji, T. 2015: Predicting postoperative complications of head and neck squamous cell carcinoma in elderly patients using random forest algorithm model. Bmc Medical Informatics and Decision Making 15: 44
Gopman, J.M.; Djajadiningrat, R.S.; Baumgarten, A.S.; Espiritu, P.N.; Horenblas, S.; Zhu, Y.; Protzel, C.; Pow-Sang, J.M.; Kim, T.; Sexton, W.J.; Poch, M.A.; Spiess, P.E. 2015: Predicting postoperative complications of inguinal lymph node dissection for penile cancer in an international multicentre cohort. Bju International 116(2): 196-201
Swenson, C.W.; Lanham, M.S.; Morgan, D.M.; Berger, M.B. 2015: Predicting postoperative day 1 hematocrit levels after uncomplicated hysterectomy. International Journal of Gynaecology and Obstetrics: the Official Organ of the International Federation of Gynaecology and Obstetrics 130(1): 19-22
Visser, L.; Prent, A.; van der Laan, M.J.; van Leeuwen, B.L.; Izaks, G.J.; Zeebregts, C.J.; Pol, R.A. 2015: Predicting postoperative delirium after vascular surgical procedures. Journal of Vascular Surgery 62(1): 183-189
Nagamatsu, Y.; Sueyoshi, S.; Tsubuku, T.; Kawasaki, M.; Akagi, Y. 2015: Predicting postoperative exercise capacity after major lung resection. Surgery Today 45(12): 1501-1508
Nomura, K.; Yamanaka, Y.; Sekine, Y.; Yamamoto, H.; Esu, Y.; Hara, M.; Hasegawa, M.; Shinnabe, A.; Kanazawa, H.; Kakuta, R.; Ozawa, D.; Hidaka, H.; Katori, Y.; Yoshida, N. 2017: Predicting postoperative fever and bacterial colonization on packing material following endoscopic endonasal surgery. European Archives of Oto-Rhino-Laryngology: Official Journal of the European Federation of Oto-Rhino-Laryngological Societies: Affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery 274(1): 167-173
Galarraga C, O.A.; Vigneron, V.; Dorizzi, B.; Khouri, N.éj.; Desailly, E. 2017: Predicting postoperative gait in cerebral palsy. Gait and Posture 52: 45-51
Galarraga C, O.A.; Vigneron, V.; Dorizzi, B.; Khouri, N.; Desailly, E. 2015: Predicting postoperative knee flexion during gait of cerebral palsy children. Computer Methods in Biomechanics and Biomedical Engineering 18 Suppl. 1: 1940-1941
Silins, V.; Brasher, C.; Antus, F.; Michelet, D.é; Hilly, J.; Grace, R.; Dahmani, S. 2017: Predicting postoperative morphine consumption in children. Anaesthesia Critical Care and Pain Medicine 36(3): 179-184
Kang, B.H.; Hwang, S.Y.; Kim, J.Y.; Hong, Y.A.; Jung, M.Y.; Lee, E.A.; Lee, J.E.; Lee, J.B.; Ko, G.J.; Pyo, H.J.; Kwon, Y.J. 2015: Predicting postoperative total calcium requirements after parathyroidectomy in secondary hyperparathyroidism. Korean Journal of Internal Medicine 30(6): 856-864
Macky, T.A.; Mohamed, A.M.H.; Emarah, A.M.; Osman, A.A.; Gado, A.S. 2015: Predicting postoperative visual outcomes in cataract patients with maculopathy. Indian Journal of Ophthalmology 63(10): 775-778
Van Meijel, E.P.M.; Gigengack, M.R.; Verlinden, E.; Opmeer, B.C.; Heij, H.A.; Goslings, J.C.; Bloemers, F.W.; Luitse, J.S.K.; Boer, F.; Grootenhuis, M.A.; Lindauer, R.ón.J.L. 2015: Predicting posttraumatic stress disorder in children and parents following accidental child injury: evaluation of the Screening Tool for Early Predictors of Posttraumatic Stress Disorder (STEPP). Bmc Psychiatry 15: 113
Pokharel, K.P.; Ludwig, T.; Storch, I. 2016: Predicting potential distribution of poorly known species with small database: the case of four-horned antelope Tetracerus quadricornis on the Indian subcontinent. Ecology and Evolution 6(8): 2297-2307
Alimi, T.O.; Fuller, D.O.; Qualls, W.A.; Herrera, S.V.; Arevalo-Herrera, M.; Quinones, M.L.; Lacerda, M.V.G.; Beier, J.C. 2015: Predicting potential ranges of primary malaria vectors and malaria in northern South America based on projected changes in climate, land cover and human population. Parasites and Vectors 8: 431
Patterson, W.L.; Peoples, B.D.; Gesten, F.C. 2015: Predicting potentially preventable hospital readmissions following bariatric surgery. Surgery for Obesity and Related Diseases: Official Journal of the American Society for Bariatric Surgery 11(4): 866-872
Blumenstock, J.; Cadamuro, G.; On, R. 2015: Predicting poverty and wealth from mobile phone metadata. Science 350(6264): 1073-1076
Hwang, J.Won.; Bae, Y.Seok.; Kang, M.Seon.; Kim, J.Hyun.; Jee, S.Ryong.; Lee, S.Heon.; An, M.Sung.; Kim, K.Hee.; Bae, K.Beom.; Kim, B.; Seol, S.Young. 2016: Predicting pre- and post-resectional histologic discrepancies in gastric low-grade dysplasia: A comparison of white-light and magnifying endoscopy. Journal of Gastroenterology and Hepatology 31(2): 394-402
Gomes, M.S.; Böttcher, D.E.; Scarparo, R.K.; Morgental, R.D.; Waltrick, S.B.G.; Ghisi, A.C.; Rahde, N.M.; Borba, M.G.; Blomberg, L.C.; Figueiredo, J.A.P. 2017: Predicting pre- and postoperative pain of endodontic origin in a southern Brazilian subpopulation: an electronic database study. International Endodontic Journal 50(8): 729-739
Chappell, L.C.; Sandall, J.; Barnard, A.M.; McManus, R.J. 2015: Predicting pre-eclampsia. Bmj 351: H6349
Jauniaux, E.; Steer, P. 2016: Predicting pre-eclampsia: 100 years of trying and failing. BJOG: an international journal of obstetrics and gynaecology 123(7): 1066
Steer, P.J. 2016: Predicting pre-eclampsia: dealing with both complex models and complex variables. Bjog: An International Journal of Obstetrics and Gynaecology 123(7): 1065
Wang, Y.; Bian, J.M.; Wang, S.N.; Nie, S.Y. 2016: Predicting precipitation on nonpoint source pollutant exports in the source area of the Liao River, China. Water Science and Technology: a Journal of the International Association on Water Pollution Research 74(4): 876-887
Levin, D.; Jun, S.H.; Dahan, M.H. 2015: Predicting pregnancy in women undergoing in-vitro fertilization with basal serum follicle stimulating hormone levels between 10.0 and 11.9 IU/L. Journal of the Turkish German Gynecological Association 16(1): 5-10
Racine, N.M.; Pillai Riddell, R.R.; Flora, D.B.; Taddio, A.; Garfield, H.; Greenberg, S. 2016: Predicting preschool pain-related anticipatory distress: the relative contribution of longitudinal and concurrent factors. Pain 157(9): 1918-1932
Meisel, M.K.; Goodie, A.S. 2015: Predicting prescription drug misuse in college students' social networks. Addictive behaviors 45: 110-112
Flanagan, M. 1993: Predicting pressure sore risk. Journal of Wound Care 2(4): 215-218
Bao, J.Lucas.; Zhang, X.; Truhlar, D.G. 2016: Predicting pressure-dependent unimolecular rate constants using variational transition state theory with multidimensional tunneling combined with system-specific quantum RRK theory: a definitive test for fluoroform dissociation. Physical Chemistry Chemical Physics: Pccp 18(25): 16659-16670
NeMoyer, A.; Brooks Holliday, S.; Goldstein, N.E.S.; McKitten, R.L. 2016: Predicting probation revocation and residential facility placement at juvenile probation review hearings: Youth-specific and hearing-specific factors. Law and human behavior 40(1): 97-105
Creswell, K.G.; Bachrach, R.L.; Wright, A.G.C.; Pinto, A.; Ansell, E. 2016: Predicting problematic alcohol use with the DSM-5 alternative model of personality pathology. Personality Disorders 7(1): 103-111
Gul, Z.; Alazem, K.; Li, I.; Monga, M. 2016: Predicting procedural pain after ureteroscopy: does hydrodistention play a role?. International Braz J Urol: Official Journal of the Brazilian Society of Urology 42(4): 734-739
Lee, C.-W.; Lin, Y.-H.; Liu, H.-M.; Wang, Y.-F.; Chen, Y.-F.; Wang, J.-L. 2016: Predicting procedure successful rate and 1-year patency after endovascular recanalization for chronic carotid artery occlusion by CT angiography. International Journal of Cardiology 221: 772-776
Avieli, H.; Ben-David, S.; Levy, I. 2016: Predicting professional quality of life among professional and volunteer caregivers. Psychological Trauma: Theory Research Practice and Policy 8(1): 80-87
Quigley, D.A.; Kristensen, V. 2015: Predicting prognosis and therapeutic response from interactions between lymphocytes and tumor cells. Molecular Oncology 9(10): 2054-2062
Hirano, Y.; Kayano, H.; Kawamata, T.; Toshida, T.; Ueda, H.; Ando, H.; Ozawa, M.; Katagiri, T.; Abe, K. 2006: Predicting prognosis based on the shape of the left ventricular cavity in dilated cardiomyopathy: analysis using rate of improvement in the circle index. Journal of Medical Ultrasonics 33(4): 217-224
Elamin, M.; Bede, P.; Montuschi, A.; Pender, N.; Chio, A.; Hardiman, O. 2015: Predicting prognosis in amyotrophic lateral sclerosis: a simple algorithm. Journal of Neurology 262(6): 1447-1454