+ Site Statistics
References:
54,258,434
Abstracts:
29,560,870
PMIDs:
28,072,757
+ Search Articles
+ Subscribe to Site Feeds
Most Shared
PDF Full Text
+ PDF Full Text
Request PDF Full Text
+ Follow Us
Follow on Facebook
Follow on Twitter
Follow on LinkedIn
+ Translate
+ Recently Requested

Analysis of Unstructured Text-Based Data Using Machine Learning Techniques: The Case of Pediatric Emergency Department Records in Nicaragua



Analysis of Unstructured Text-Based Data Using Machine Learning Techniques: The Case of Pediatric Emergency Department Records in Nicaragua



Medical Care Research and Review 2019: 1077558719844123



Free-text information is still widely used in emergency department (ED) records. Machine learning techniques are useful for analyzing narratives, but they have been used mostly for English-language data sets. Considering such a framework, the performance of an ML classification task of a Spanish-language ED visits database was tested. ED visits collected in the EDs of nine hospitals in Nicaragua were analyzed. Spanish-language, free-text discharge diagnoses were considered in the analysis. Five-hundred random forests were trained on a set of bootstrap samples of the whole data set (1,789 ED visits) to perform the classification task. For each one, after having identified optimal parameter value, the final validated model was trained on the whole bootstrapped data set and tested. The classification accuracies had a median of 0.783 (95% CI [0.779, 0.796]). Machine learning techniques seemed to be a promising opportunity for the exploitation of unstructured information reported in ED records in low- and middle-income Spanish-speaking countries.

(PDF emailed within 0-6 h: $19.90)

Accession: 066731859

Download citation: RISBibTeXText

PMID: 31030615

DOI: 10.1177/1077558719844123


Related references

Extracting diagnoses and investigation results from unstructured text in electronic health records by semi-supervised machine learning. Plos One 7(1): E30412, 2012

Comparison of machine learning classifiers for influenza detection from emergency department free-text reports. Journal of Biomedical Informatics 58: 60-69, 2016

A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage. Academic Emergency Medicine 2018, 2018

Improving sensitivity of machine learning methods for automated case identification from free-text electronic medical records. Bmc Medical Informatics and Decision Making 13: 30, 2013

Emergency Department Visit Forecasting and Dynamic Nursing Staff Allocation Using Machine Learning Techniques With Readily Available Open-Source Software. Computers, Informatics, Nursing 33(8): 368-377, 2016

Machine Learning-Based Prediction of Clinical Outcomes for Children During Emergency Department Triage. JAMA Network Open 2(1): E186937-E186937, 2019

A customized method for information extraction from unstructured text data in the electronic medical records. Beijing Da Xue Xue Bao. Yi Xue Ban 50(2): 256-263, 2018

Prediction of In-hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data-Driven, Machine Learning Approach. Academic Emergency Medicine 23(3): 269-278, 2016

How much information is lost during processing? A case study of pediatric emergency department records. Computers and Biomedical Research, An International Journal 26(6): 582-591, 1993

A novel behavioral model of the pasture-based dairy cow from GPS data using data mining and machine learning techniques. Journal of Dairy Science 99(3): 2063-2075, 2016

Learning signals of adverse drug-drug interactions from the unstructured text of electronic health records. AMIA Joint Summits on Translational Science Proceedings. AMIA Joint Summits on Translational Science 2013: 98, 2013

Pediatric blunt abdominal trauma in the emergency department: evidence-based management techniques. Pediatric Emergency Medicine Practice 11(10): 1-23; Quiz 23-4, 2015

Changing state of pediatric injuries in South Africa: An analysis of surveillance data from a Pediatric Emergency Department from 2007 to 2011. Surgery 162(6s): S4-S11, 2017

Learning gains derived from a high-fidelity mannequin-based simulation in the pediatric emergency department. Journal of the Formosan Medical Association 105(1): 94-98, 2006

Predicting return visits to the emergency department for pediatric patients: Applying supervised learning techniques to the Taiwan National Health Insurance Research Database. Computer Methods and Programs in Biomedicine 144: 105-112, 2017