Predicting hospitalization of pediatric asthma patients in emergency departments using machine learning
| Autor: | Marion R. Sills, Mustafa Ozkaynak, Hoon Jang |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Male
020205 medical informatics media_common.quotation_subject Health Informatics 02 engineering and technology Machine learning computer.software_genre Logistic regression Machine Learning 03 medical and health sciences 0302 clinical medicine 0202 electrical engineering electronic engineering information engineering Humans Quality (business) 030212 general & internal medicine Child media_common Receiver operating characteristic business.industry Emergency department Triage Asthma Emergency Severity Index Random forest Test (assessment) Hospitalization Artificial intelligence business Emergency Service Hospital computer |
| Zdroj: | International journal of medical informatics. 151 |
| ISSN: | 1872-8243 |
| Popis: | Motivation The timely identification of patients for hospitalization in emergency departments (EDs) can facilitate efficient use of hospital resources. Machine learning can help the early prediction of ED disposition; however, application of machine learning models requires both computer science skills and domain knowledge. This presents a barrier for those who want to apply machine learning to real-world settings. Objectives The objective of this study was to construct a competitive predictive model with a minimal amount of human effort to facilitate decisions regarding hospitalization of patients. Methods This study used the electronic health record data from five EDs in a single healthcare system, including an academic urban children’s hospital ED, from January 2009 to December 2013. We constructed two machine learning models by using automated machine learning algorithm (autoML) which allows non-experts to use machine learning model: one with data only available at ED triage, the other adding information available one hour into the ED visit. Random forest and logistic regression were employed as bench-marking models. The ratio of the training dataset to the test dataset was 8:2, and the area under the receiver operating characteristic curve (AUC), accuracy, and F1 were calculated to assess the quality of the models. Results Of the 9,069 ED visits analyzed, the study population was made up of males (62.7 %), median (IQR) age was 6 (4, 10) years, and public insurance coverage (66.0 %). The majority had an Emergency Severity Index score of 3 (52.9 %). The prevalence of hospitalization was 22.5 %. The AUCs were 0.914 and 0.942. AUCs were 0.831 and 0.886 for random forests, and 0.795 and 0.823 for logistic regression. Among the predictors, an outcome at a prior visit, ESI level, time to first medication, and time to triage were the most important features for the prediction of the need for hospitalization. Conclusions In comparison with the conventional approaches, the use of autoML improved the predictive ability for the need for hospitalization. The findings can optimize ED management, hospital-level resource utilization and improve quality. Furthermore, this approach can support the design of a more effective patient ED flow for pediatric asthma care. |
| Databáze: | OpenAIRE |
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