Identifying Preventable Emergency Admissions in Hospitals Using Machine Learning

Journal Article

2023

Published in: Identifying Preventable Emergency Admissions in Hospitals Using Machine Learning

AI in emergency Medicine f

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.

• Publisher Name: IOS
• Publishing City: Italy