Identifying Preventable Emergency Admissions in Hospitals Using Machine Learning.

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.