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Section 72

EurekaMag Full Text Articles Chapter 71,444



References:

Rueda, P. 1948: Classification test for diseases of the child. Revista Argentina de Puericultura e Neonatologia 14(45): 60
Jayle, G.E.; Ourgaud, A.G. 1949: Classification test for oculomotor disorders of the horizontal gaze; about two new cases of atypical Foville syndrome. Revue d'Oto-Neuro-Ophtalmologie 21(2): 86-93
Botelho, M.éi.L.; Correia, M.D.L.; Manzoli, J.P.B.; Montanari, F.áb.L.; Carvalho, L.A.C.; Duran, E.C.M. 2021: Classification tree for the inference of the nursing diagnosis Fluid Volume Excess (00026). Revista da Escola de Enfermagem da U S P 55: E03682
Patrone, P.N.; Kearsley, A.J. 2021: Classification under uncertainty: data analysis for diagnostic antibody testing. Mathematical Medicine and Biology: a Journal of the Ima 38(3): 396-416
Feis, R.A.; van der Grond, J.; Bouts, M.J.R.J.; Panman, J.L.; Poos, J.M.; Schouten, T.M.; de Vos, F.; Jiskoot, L.C.; Dopper, E.G.P.; van Buchem, M.A.; van Swieten, J.C.; Rombouts, S.A.R.B. 2020: Classification using fractional anisotropy predicts conversion in genetic frontotemporal dementia, a proof of concept. Brain Communications 2(2): Fcaa 079
Liu, M.; Matsumura, N.; Mandai, M.; Li, K.; Yagi, H.; Baba, T.; Suzuki, A.; Hamanishi, J.; Fukuhara, K.; Konishi, I. 2009: Classification using hierarchical clustering of tumor-infiltrating immune cells identifies poor prognostic ovarian cancers with high levels of COX expression. Modern Pathology: An Official Journal of the United States and Canadian Academy of Pathology Inc 22(3): 373-384
Poddubnyy, D. 2020: Classification vs diagnostic criteria: the challenge of diagnosing axial spondyloarthritis. Rheumatology 59(Suppl 4): Iv6
Chen, T.; van Gelder, J.; van de Ven, B.; Amitonov, S.V.; de Wilde, B.; Ruiz Euler, H-Christian.; Broersma, H.; Bobbert, P.A.; Zwanenburg, F.A.; van der Wiel, W.G. 2020: Classification with a disordered dopant-atom network in silicon. Nature 577(7790): 341-345
Thompson, G.Z.; Maitra, R.; Meeker, W.Q.; Bastawros, A.F. 2020: Classification with the matrix-variate- t distribution. Journal of Computational and Graphical Statistics: a Joint Publication of American Statistical Association Institute of Mathematical Statistics Interface Foundation of North America 29(3): 668-674
Leary, E.; Stoker, A.M.; Cook, J.L. 2020: Classification, Categorization, and Algorithms for Articular Cartilage Defects. Journal of Knee Surgery 33(11): 1069-1077
Mouchet, J.; Betts, K.A.; Georgieva, M.V.; Ionescu-Ittu, R.; Butler, L.M.; Teitsma, X.; Delmar, P.; Kulalert, T.; Zhu, J.; Lema, N.; Desai, U. 2021: Classification, Prediction, and Concordance of Cognitive and Functional Progression in Patients with Mild Cognitive Impairment in the United States: a Latent Class Analysis. Journal of Alzheimer's Disease: Jad 82(4): 1667-1682
Tahamtan, Y.; Mirghafari, H. 2016: Classification, capsular PCR typing and genetic diversity of Pasteurella multocida isolated from sheep and goats in Iran using Bam HI, Hind IIi restriction endonuclease enzymes by PCR-RFLP. Tropical Biomedicine 33(3): 535-542
Vague, J.; Temime-Morhange, A. 1957: Classification, diagnosis and treatment of hypercorticisms. Marseille Medical 94(2): 71-99
Tarasenkov, A.O. 2021: Classification, diagnosis and treatment of juvenile glaucoma. Vestnik Oftalmologii 137(4): 123-127
Alessandri, H.; Etcheverry, R. 1948: Classification, diagnosis and treatment of the most frequent anemias. Revista Medica de Chile 76(3): 105-120
Valavanis, C.; Stanc, G.M.; Baltayiannis, N. 2021: Classification, histopathology and molecular pathology of thymic epithelial tumors: a review. Journal of Buon: Official Journal of the Balkan Union of Oncology 26(4): 1198-1207
Xu, Z.; Jiang, Y.; Ji, J.; Forsberg, E.; Li, Y.; He, S. 2020: Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning. Optics Express 28(21): 30686-30700
Masson, P. 1953: Classification, interpretation and diagnosis of testicular tumors. Acta - Unio Internationalis Contra Cancrum 9(4): 864-871
Mella, M. 1953: Classification, interpretation and diagnosis of testicular tumors. Acta - Unio Internationalis Contra Cancrum 9(4): 875-876
Li, J.; Yin, Y.X.; Zhao, C.Q.; Liu, C.Y.; Hou, H.C. 2021: Classification, pathological features and clinical significance of placental inflammatory lesions. Zhonghua Bing Li Xue Za Zhi 50(3): 300-304
Shon, D.J.; Kuo, A.; Ferracane, M.J.; Malaker, S.A. 2021: Classification, structural biology, and applications of mucin domain-targeting proteases. Biochemical Journal 478(8): 1585-1603
Varzandian, A.; Razo, M.A.S.; Sanders, M.R.; Atmakuru, A.; Di Fatta, G. 2021: Classification-Biased Apparent Brain Age for the Prediction of Alzheimer's Disease. Frontiers in Neuroscience 15: 673120
Fu, B.; Zhang, J.; Wang, S.; Zhao, W. 2020: Classification-coordination-collaboration: a systems approach for advancing Sustainable Development Goals. National Science Review 7(5): 838-840
Wilks, C. 2005: Classifications. British Dental Journal 198(9): 561
Candia, R. 2020: Classifications and scores used in digestive endoscopy. Revista Medica de Chile 148(7): 992-1003
Truong, M.; Lennartsson, F.; Bibic, A.; Sundius, L.; Persson, A.; Siemund, R.; In't Zandt, R.é; Goncalves, I.; Wassélius, J. 2021: Classifications of atherosclerotic plaque components with T1 and T2* mapping in 11.7 T MRi. European Journal of Radiology Open 8: 100323
Riddle, D.L.; Dumenci, L. 2020: Classifications of good versus poor outcome following knee arthroplasty should not be defined using arbitrary criteria. Bmc Musculoskeletal Disorders 21(1): 604
Bottós, J.; Elizalde, J.; Rodrigues, E.B.; Farah, M.; Maia, M. 2014: Classifications of vitreomacular traction syndrome: diameter vs morphology. Eye 28(9): 1107-1112
Tötsch, N.; Hoffmann, D. 2021: Classifier uncertainty: evidence, potential impact, and probabilistic treatment. PEERJ. Computer Science 7: E398
Liu, J.; Wang, X.; Lin, J.; Li, S.; Deng, G.; Wei, J. 2021: Classifiers for Predicting Coronary Artery Disease Based on Gene Expression Profiles in Peripheral Blood Mononuclear Cells. International Journal of General Medicine 14: 5651-5663
Huang, W.; Wang, L.; Yan, Z.; Liu, Y. 2020: Classify Motor Imagery by a Novel CNN with Data Augmentation. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2020: 192-195
Deng, F.; Shen, L.; Wang, H.; Zhang, L. 2020: Classify multicategory outcome in patients with lung adenocarcinoma using clinical, transcriptomic and clinico-transcriptomic data: machine learning versus multinomial models. American Journal of Cancer Research 10(12): 4624-4639
Kuhn, J.H.; Wolf, Y.I.; Krupovic, M.; Zhang, Y-Zhen.; Maes, P.; Dolja, V.V.; Koonin, E.V. 2019: Classify viruses - the gain is worth the pain. Nature 566(7744): 318-320
Bertò, G.; Bullock, D.; Astolfi, P.; Hayashi, S.; Zigiotto, L.; Annicchiarico, L.; Corsini, F.; De Benedictis, A.; Sarubbo, S.; Pestilli, F.; Avesani, P.; Olivetti, E. 2021: Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation. Neuroimage 224: 117402
Wang, L.; Zhao, Z.; Luo, Y.; Yu, H.; Wu, S.; Ren, X.; Zheng, C.; Huang, X. 2020: Classifying 2-year recurrence in patients with dlbcl using clinical variables with imbalanced data and machine learning methods. Computer Methods and Programs in Biomedicine 196: 105567
Thomas, R.M.; Gallo, S.; Cerliani, L.; Zhutovsky, P.; El-Gazzar, A.; van Wingen, G. 2020: Classifying Autism Spectrum Disorder Using the Temporal Statistics of Resting-State Functional MRi Data with 3D Convolutional Neural Networks. Frontiers in Psychiatry 11: 440
Dankner, M.; Rose, A.A.N.; Rajkumar, S.; Siegel, P.M.; Watson, I.R. 2018: Classifying BRAF alterations in cancer: new rational therapeutic strategies for actionable mutations. Oncogene 37(24): 3183-3199
Willis, A.D.; Peek, R.A.; Rypel, A.L. 2021: Classifying California's stream thermal regimes for cold-water conservation. Plos one 16(8): E0256286
Van Klij, P.; Reiman, M.P.; Waarsing, J.H.; Reijman, M.; Bramer, W.M.; Verhaar, J.A.N.; Agricola, R. 2020: Classifying Cam Morphology by the Alpha Angle: a Systematic Review on Threshold Values. Orthopaedic Journal of Sports Medicine 8(8): 2325967120938312
Klineberg, E.O.; Passias, P.G.; Poorman, G.W.; Jalai, C.M.; Atanda, A.; Worley, N.; Horn, S.; Sciubba, D.M.; Hamilton, D.K.; Burton, D.C.; Gupta, M.C.; Smith, J.S.; Soroceanu, A.; Hart, R.A.; Neuman, B.; Ames, C.P.; Schwab, F.J.; Lafage, V. 2020: Classifying Complications: Assessing Adult Spinal Deformity 2-Year Surgical Outcomes. Global Spine Journal 10(7): 896-907
Lin, S.-N.; Zheng, D.-P.; Qiu, Y.; Zhang, S.-H.; He, Y.; Chen, B.-L.; Zeng, Z.-R.; Mao, R.; Chen, M.-H. 2020: Classifying Crohn's disease into colon-involving versus non-colon-involving groups is a better predictor of clinical outcomes than the Montreal classification. Therapeutic Advances in Gastroenterology 13: 1756284820968732
Van Damme, R.; Coli, G.M.; van Roij, R.é; Dijkstra, M. 2020: Classifying Crystals of Rounded Tetrahedra and Determining their Order Parameters Using Dimensionality Reduction. Acs Nano 14(11): 15144-15153
Spahn, C.; Krampe, F.; Nusseck, M. 2021: Classifying Different Types of Music Performance Anxiety. Frontiers in Psychology 12: 538535
Oettershagen, L.; Kriege, N.M.; Morris, C.; Mutzel, P. 2020: Classifying Dissemination Processes in Temporal Graphs. Big Data 8(5): 363-378
Ross, G.B.; Dowling, B.; Troje, N.F.; Fischer, S.L.; Graham, R.B. 2020: Classifying Elite from Novice Athletes Using Simulated Wearable Sensor Data. Frontiers in Bioengineering and Biotechnology 8: 814
Perrier, S.; Matovic, S.; Bernard, G.èv. 2020: Classifying Hypomyelination: a Critical (White) Matter. Child Neurology Open 7: 2329048x20983761
Prendiville, O.; Walton, J.; Flynn, A.; Nugent, A.P.; McNulty, B.A.; Brennan, L. 2021: Classifying Individuals into a Dietary Pattern Based on Metabolomic Data. Molecular Nutrition and Food Research 65(11): E2001183
Dunlap, C.F.; Colachis, S.C.; Meyers, E.C.; Bockbrader, M.A.; Friedenberg, D.A. 2020: Classifying Intracortical Brain-Machine Interface Signal Disruptions Based on System Performance and Applicable Compensatory Strategies: a Review. Frontiers in Neurorobotics 14: 558987
Mohebbian, M.; Walia, E.; Habibullah, M.; Stapleton, S.; Wahid, K.A. 2021: Classifying MRi motion severity using a stacked ensemble approach. Magnetic Resonance Imaging 75: 107-115
Gershanov, S.; Madiwale, S.; Feinberg-Gorenshtein, G.; Vainer, I.; Nehushtan, T.; Michowiz, S.; Goldenberg-Cohen, N.; Birger, Y.; Toledano, H.; Salmon-Divon, M. 2021: Classifying Medulloblastoma Subgroups Based on Small, Clinically Achievable Gene Sets. Frontiers in Oncology 11: 637482
Akella, A.; Singh, A.K.; Leong, D.; Lal, S.; Newton, P.; Clifton-Bligh, R.; Mclachlan, C.S.; Gustin, S.M.; Maharaj, S.; Lees, T.; Cao, Z.; Lin, C.-T. 2021: Classifying Multi-Level Stress Responses from Brain Cortical EEG in Nurses and Non-Health Professionals Using Machine Learning Auto Encoder. IEEE Journal of Translational Engineering in Health and Medicine 9: 2200109
Davoudi, A.; Dion, C.; Amini, S.; Tighe, P.J.; Price, C.C.; Libon, D.J.; Rashidi, P. 2021: Classifying Non-Dementia and Alzheimer's Disease/Vascular Dementia Patients Using Kinematic, Time-Based, and Visuospatial Parameters: the Digital Clock Drawing Test. Journal of Alzheimer's Disease: Jad 82(1): 47-57
Baez, S.; Herrera, E.; Trujillo, C.; Cardona, J.F.; Diazgranados, J.A.; Pino, M.; Santamaría-García, H.; Ibáñez, A.ín.; García, A.M. 2020: Classifying Parkinson's Disease Patients with Syntactic and Socio-emotional Verbal Measures. Frontiers in Aging Neuroscience 12: 586233
Irfan, A.; Adivishnu, A.L.; Sze-To, A.; Dehkharghanian, T.; Rahnamayan, S.; Tizhoosh, H.R. 2020: Classifying Pneumonia among Chest X-Rays Using Transfer Learning. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2020: 2186-2189
Zhu, D.; Chen, Y.; Zheng, K.; Chen, C.; Li, Q.; Zhou, J.; Jia, X.; Xia, N.; Wang, H.; Lin, B.; Ni, Y.; Pang, P.; Yang, Y. 2021: Classifying Ruptured Middle Cerebral Artery Aneurysms with a Machine Learning Based, Radiomics-Morphological Model: a Multicentral Study. Frontiers in Neuroscience 15: 721268
Fritz, K.M.; Nadeau, T.-L.; Kelso, J.E.; Beck, W.S.; Mazor, R.D.; Harrington, R.A.; Topping, B.J. 2020: Classifying Streamflow Duration: the Scientific Basis and an Operational Framework for Method Development. Water 12(9): 1-2545
Vizer, L.M.; Sears, A. 2015: Classifying Text-Based Computer Interactions for Health Monitoring. IEEE Pervasive Computing 14(4): 64-71
Xu, Y.; Yu, P.; Zheng, J.; Wang, C.; Hu, T.; Yang, Q.; Xu, Z.; Guo, F.; Tang, X.; Ren, F.; Zhu, Y. 2021: Classifying Vulnerability to Sleep Deprivation Using Resting-State Functional MRi Graph Theory Metrics. Frontiers in Neuroscience 15: 660365
Fujiki, D.; Kikuzawa, K. 2004: Classifying aerial stems of woody plants by developmental stages using relative growth rate. New Phytologist 161(2): 427-433
Gaiser, T.; Kutzner, H.; Palmedo, G.; Siegelin, M.D.; Wiesner, T.; Bruckner, T.; Hartschuh, W.; Enk, A.H.; Becker, M.R. 2010: Classifying ambiguous melanocytic lesions with FISH and correlation with clinical long-term follow up. Modern Pathology: An Official Journal of the United States and Canadian Academy of Pathology Inc 23(3): 413-419
Cai, M.; Huang, H.; Xu, L.; Lin, N. 2021: Classifying and Evaluating Fetuses with Ventriculomegaly in Genetic Etiologic Studies. Frontiers in Genetics 12: 682707
Koenen, L.; Arens, P.; Olze, H.; Dommerich, S. 2021: Classifying and Predicting Surgical Complications After Laryngectomy: a Novel Approach to Diagnosing and Treating Patients. Ear Nose and Throat Journal 2021: 1455613211029749
Regidor, E.; Cea-Soriano, Lía.; Ruiz, A.; Goday, A.; Carabantes, D.; Díez-Espino, J.; Artola, S.; Franch-Nadal, J. 2021: Classifying and communicating risks in prediabetes according to fasting glucose and/or glycated hemoglobin: PREDAPS cohort study. Scandinavian Journal of Primary Health Care 2021: 1-9
Aringer, M.; Johnson, S.R. 2020: Classifying and diagnosing systemic lupus erythematosus in the 21st century. Rheumatology 59(Suppl 5): V4
Espinoza Bernal, V.C.; Hiremath, S.V.; Wolf, B.; Riley, B.; Mendonca, R.J.; Johnson, M.J. 2021: Classifying and tracking rehabilitation interventions through machine-learning algorithms in individuals with stroke. Journal of Rehabilitation and Assistive Technologies Engineering 8: 20556683211044640
Chua, E.Chern-Pin.; Sullivan, J.P.; Duffy, J.F.; Klerman, E.B.; Lockley, S.W.; Kristal, B.S.; Czeisler, C.A.; Gooley, J.J. 2019: Classifying attentional vulnerability to total sleep deprivation using baseline features of Psychomotor Vigilance Test performance. Scientific Reports 9(1): 12102
Soultan, A.; Wikelski, M.; Safi, K. 2020: Classifying biogeographic realms of the endemic fauna in the Afro-Arabian region. Ecology and Evolution 10(16): 8669-8680
Underbakke, E.S.; Kiessling, L.L. 2010: Classifying chemoreceptors: quantity versus quality. EMBO Journal 29(20): 3435-3436
Gilam, G.; Cramer, E.M.; Webber, K.A.; Ziadni, M.S.; Kao, M.-C.; Mackey, S.C. 2021: Classifying chronic pain using multidimensional pain-agnostic symptom assessments and clustering analysis. Science Advances 7(37): Eabj 0320
Friedl, P.; Locker, J.; Sahai, E.; Segall, J.E. 2012: Classifying collective cancer cell invasion. Nature Cell Biology 14(8): 777-783
Stevens, C.E.; Zabelina, D.L. 2020: Classifying creativity: Applying machine learning techniques to divergent thinking EEG data. Neuroimage 219: 116990
Amiruzzaman, M.; Curtis, A.; Zhao, Y.; Jamonnak, S.; Ye, X. 2021: Classifying crime places by neighborhood visual appearance and police geonarratives: a machine learning approach. Journal of Computational Social Science 4(2): 813-837
Wang, Z.; Li, C. 2020: Classifying cross-frequency coupling pattern in epileptogenic tissues by convolutional neural network. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2020: 3440-3443
Bathini, P.; Foucras, S.; Dupanloup, I.; Imeri, H.; Perna, A.; Berruex, J.-L.; Doucey, M.-A.; Annoni, J.-M.; Auber Alberi, L. 2020: Classifying dementia progression using microbial profiling of saliva. Alzheimer's and Dementia 12(1): E12000
Lin, Z.; Lawrence, W.R.; Huang, Y.; Lin, Q.; Gao, Y. 2021: Classifying depression using blood biomarkers: a large population study. Journal of Psychiatric Research 140: 364-372
Prieto Santamaría, Lía.; García Del Valle, E.P.; Zanin, M.; Hernández Chan, G.Samuel.; Pérez Gallardo, Y.; Rodríguez-González, A. 2021: Classifying diseases by using biological features to identify potential nosological models. Scientific Reports 11(1): 21096
Porta, M.; Kim, S.; Pau, M.; Nussbaum, M.A. 2021: Classifying diverse manual material handling tasks using a single wearable sensor. Applied Ergonomics 93: 103386
Karimi, K.; Agalakov, S.; Telmer, C.A.; Beatman, T.R.; Pells, T.J.; Arshinoff, B.I.; Ku, C.J.; Foley, S.; Hinman, V.F.; Ettensohn, C.A.; Vize, P.D. 2021: Classifying domain-specific text documents containing ambiguous keywords. Database: the Journal of Biological Databases and Curation 2021
Raio, C.M.; Goldfarb, E.V.; Lempert, K.M.; Sokol-Hessner, P. 2016: Classifying emotion regulation strategies. Nature Reviews. Neuroscience 17(8): 532
Shlobin, N.A.; Singh, G.; Newton, C.R.; Sander, J.W. 2021: Classifying epilepsy pragmatically: Past, present, and future. Journal of the Neurological Sciences 427: 117515
Bazhenov, N.; Mustafa, M.; San Mauro, L.; Sorbi, A.; Yamaleev, M. 2020: Classifying equivalence relations in the Ershov hierarchy. Archive for Mathematical Logic 59(7): 835-864
van Beest, F.M.; Mews, S.; Elkenkamp, S.; Schuhmann, P.; Tsolak, D.; Wobbe, T.; Bartolino, V.; Bastardie, F.; Dietz, R.; von Dorrien, C.; Galatius, A.; Karlsson, O.; McConnell, B.; Nabe-Nielsen, J.; Olsen, M.Tange.; Teilmann, J.; Langrock, R. 2019: Classifying grey seal behaviour in relation to environmental variability and commercial fishing activity - a multivariate hidden Markov model. Scientific Reports 9(1): 5642
Liew, B.X.W.; Rugamer, D.; Abichandani, D.; De Nunzio, A.M. 2020: Classifying individuals with and without patellofemoral pain syndrome using ground force profiles - Development of a method using functional data boosting. Gait and Posture 80: 90-95
Nikolaidis, G.F.; Woods, B.; Palmer, S.; Soares, M.O. 2021: Classifying information-sharing methods. Bmc Medical Research Methodology 21(1): 107
Hsu, W.-F.; Hsu, C.-Y.; Yen, A.M.-F.; Chen, S.L.-S.; Chiu, S.Y.-H.; Fann, J.C.-Y.; Lee, Y.-C.; Chiu, H.-M.; Chen, H.-H. 2021: Classifying interval cancers as false negatives or newly occurring in fecal immunochemical testing. Journal of Medical Screening 28(3): 286-294
Teeter, B.S.; Thomas, J.L.; Curran, G.M.; Balamurugan, A. 2020: Classifying intervention modifications in the community pharmacy context: a demonstration study. Journal of the American Pharmacists Association: Japha 60(6): E230-E235
Cao, H.; Wang, J.; Baranova, A.; Zhang, F. 2021: Classifying major mental disorders genetically. ProgressinNeuro-PsychopharmacologyandBiologicalPsychiatry 2021: 110410
Plona, K.L.; Eastman, J.F.; Drumm, M.L. 2021: Classifying molecular phenotypes of G6PC variants for pathogenic properties and to guide therapeutic development. Jimd Reports 60(1): 56-66
Mlika, M.; Zorgati, M.; Mezni, F.El. 2020: Classifying multiple lung cancers using morphological features: a meta-analysis. Journal of Immunoassay and Immunochemistry 41(5): 817-832
Buyukturkoglu, K.; Zeng, D.; Bharadwaj, S.; Tozlu, C.; Mormina, E.; Igwe, K.C.; Lee, S.; Habeck, C.; Brickman, A.M.; Riley, C.S.; De Jager, P.L.; Sumowski, J.F.; Leavitt, V.M. 2021: Classifying multiple sclerosis patients on the basis of SDMT performance using machine learning. Multiple Sclerosis 27(1): 107-116
Kearney, K.M.; Harley, J.B.; Nichols, J.A. 2021: Classifying muscle parameters with artificial neural networks and simulated lateral pinch data. Plos one 16(9): E0255103
Capecchi, A.; Reymond, J.-L. 2021: Classifying natural products from plants, fungi or bacteria using the COCONUT database and machine learning. Journal of Cheminformatics 13(1): 82
Sachdev, P.S.; Blacker, D.; Blazer, D.G.; Ganguli, M.; Jeste, D.V.; Paulsen, J.S.; Petersen, R.C. 2014: Classifying neurocognitive disorders: the DSM-5 approach. Nature Reviews. Neurology 10(11): 634-642
Alex, B.; Whyte, D.; Duma, D.; Owen, R.E.; Fairley, E.A.L. 2021: Classifying patient and professional voice in social media health posts. Bmc Medical Informatics and Decision Making 21(1): 244
Pardee, G. 1969: Classifying patients to predict staff requirements. Hu Li Za Zhi Journal of Nursing 16(3): 36-39
Kashihara, J.; Takebayashi, Y.; Kunisato, Y.; Ito, M. 2021: Classifying patients with depressive and anxiety disorders according to symptom network structures: a Gaussian graphical mixture model-based clustering. Plos one 16(9): E0256902
Chang, Y.; Huynh, C.T.T.; Bastin, K.M.; Rivera, B.N.; Siddens, L.K.; Tilton, S.C. 2020: Classifying polycyclic aromatic hydrocarbons by carcinogenic potency using in vitro biosignatures. Toxicology in Vitro: An International Journal Published in Association with Bibra 69: 104991
Zhang, J.; Richardson, J.Don.; Dunkley, B.T. 2020: Classifying post-traumatic stress disorder using the magnetoencephalographic connectome and machine learning. Scientific Reports 10(1): 5937
Serner, A.; Arnaiz, J.; Mosler, A.; Almusa, E.; Vuckovic, Z.; Tak, I.; Maas, M.; Weir, A.; Hölmich, P. 2021: Classifying radiographic changes of the pubic symphysis in male athletes: Development and reproducibility of a new scoring protocol. European Journal of Radiology 134: 109452
Ivanenko, A.; Watkins, P.; van Gerven, M.A.J.; Hammerschmidt, K.; Englitz, B. 2020: Classifying sex and strain from mouse ultrasonic vocalizations using deep learning. Plos Computational Biology 16(6): E1007918
Filippi, M.; Rocca, M.A. 2019: Classifying silent progression in relapsing-remitting MS. Nature Reviews. Neurology 15(6): 315-316
Azizzadeh-Roodpish, S.; Garzon, M.H.; Mainali, S. 2021: Classifying single nucleotide polymorphisms in humans. Molecular Genetics and Genomics: Mgg 296(5): 1161-1173
Ismail, R.; Voss, J.G.; Yona, S.; Nurachmah, E.; Boutain, D.; Lowe, C.; John-Stewart, G.; Woods, N.Fugate. 2021: Classifying stigma experience of women living with HIV in Indonesia through the social ecological model. Health Care for Women International 2021: 1-22
Talantsev, E.F.; Mataira, R.C.; Crump, W.P. 2020: Classifying superconductivity in Moiré graphene superlattices. Scientific Reports 10(1): 212
Song, K. 2020: Classifying the Lifestyle of Metagenomically-Derived Phages Sequences Using Alignment-Free Methods. Frontiers in Microbiology 11: 567769
Song, Y.C.; Lee, S.E.; Jin, Y.; Park, H.W.; Chun, K.-H.; Lee, H.-W. 2020: Classifying the Linkage between Adipose Tissue Inflammation and Tumor Growth through Cancer-Associated Adipocytes. Molecules and Cells 43(9): 763-773
Ocampo, L.; Tanaid, R.A.; Tiu, A.M.; Selerio, E.; Yamagishi, K. 2021: Classifying the degree of exposure of customers to COVID-19 in the restaurant industry: a novel intuitionistic fuzzy set extension of the TOPSIS-Sort. Applied Soft Computing 113: 107906
Van Engelen, T.S.R.; Kanglie, M.M.N.P.; van den Berk, I.A.H.; Bouwman, M.L.J.; Suhooli, H.J.M.; Heckert, S.L.; Stoker, J.; Bossuyt, P.M.M.; Prins, J.M.; Annema, J.; Beenen, L.F.M.; van den Berk, I.A.H.; Bipat, S.; Bossuyt, P.M.M.; Bresser, P.; Dijkgraaf, M.; Donker, J.; van Engelen, T.S.R.; Frankemölle, B.; Groenink, M.; Hochheimer, S.M.R.; Holleman, F.; Hulzebosch, D.; Kanglie, M.M.N.P.; Keijzers, M.; van der Lee, I.; Leenhouts, P.; Luitse, J.; Meijboom, L.J.; Middeldorp, S.; Montauban van Swijndregt, A.; de Monyé, W.; Otker, J.; Ridderikhof, M.; Romijn, J.A.; Schoonderwoerd, A.J.N.; Sprengers, R.W.; Stoker, J.; Taal, E.M.; Winter, M.; Prins, J.M. 2020: Classifying the diagnosis of study participants in clinical trials: a structured and efficient approach. European Radiology Experimental 4(1): 44
Mann, D.L.; Tweedy, S.M.; Jackson, R.C.; Vanlandewijck, Y.C. 2021: Classifying the evidence for evidence-based classification in Paralympic sport. Journal of Sports Sciences 39(Sup1): 1-6
Maley, C.C.; Aktipis, A.; Graham, T.A.; Sottoriva, A.; Boddy, A.M.; Janiszewska, M.; Silva, A.S.; Gerlinger, M.; Yuan, Y.; Pienta, K.J.; Anderson, K.S.; Gatenby, R.; Swanton, C.; Posada, D.; Wu, C-I.; Schiffman, J.D.; Hwang, E.Shelley.; Polyak, K.; Anderson, A.R.A.; Brown, J.S.; Greaves, M.; Shibata, D. 2017: Classifying the evolutionary and ecological features of neoplasms. Nature Reviews. Cancer 17(10): 605-619
Ricciardi, C.; Improta, G.; Amato, F.; Cesarelli, G.; Romano, M. 2020: Classifying the type of delivery from cardiotocographic signals: a machine learning approach. Computer Methods and Programs in Biomedicine 196: 105712
Waxman, A.J.; Mick, R.; Garfall, A.L.; Cohen, A.; Vogl, D.T.; Stadtmauer, E.A.; Weiss, B.M. 2015: Classifying ultra-high risk smoldering myeloma. Leukemia 29(3): 751-753
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