AI-powered models for overcrowding prediction at TUMS hospitals.

ABSTRACT: When the emergency department (ED) of a hospital reaches its saturation capacity, adverse events such as increased waiting times, increased mortality, clinical mistakes, and a financial burden escalate. Accurate overcrowding forecasting renders a powerful alarming tool for increasing the operational efficiency of hospitals. This study provides a novel contribution by leveraging a time series dataset for 5 hospitals of Tehran University of Medical Sciences (TUMS) to predict daily ED-overcrowding. Firstly, we identified 121 possible features per ED. Then, in the attribute selection, we used a new time series features selection method using minimum redundancy and maximum relevance (mRMR) with mutual information selector, and elastic net, both implemented via time series cross validation with

SUBMITTER: Shaibani MJ 

PROVIDER: S-EPMC12780012 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature