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Identifying Preventable Emergency Admissions in Hospitals Using Machine Learning
Sarah A. Alkhodair, Norah Altwaijri, Ahmed I. Albarrak
95 - 96
10.3233/SHTI230747
Research Article
Studies in Health Technology and Informatics
Volume 309: Telehealth Ecosystems in Practice
Abstract

Overcrowding in EDs has been viewed globally as a chronic health challenge. It is directly related to the increased use of EDs for non-urgent issues, leading to increased complications, long waiting times, a higher death rate, or delayed intervention of those more acutely ill. This study aims to develop Machine Learning models to differentiate immediate medical needs from unnecessary ED visits. A Decision Tree, Random Forest, AdaBoost, and XGBoost models were built and evaluated on real-life data. XGBoost achieved the best accuracy and F1-score.

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