Application of Machine Learning Algorithms for Patient Length of Stay Prediction in Emergency Department During Hajj

Abstract

Hospital emergency departments (EDs) in vital locations face high patient demand during peak events such as the annual Islamic pilgrimage (the Hajj event) in Mecca, Saudi Arabia, the New Year celebration ceremony in New York, and the World Cup, etc. Variable patient arrival rates and hospital conditions, particularly the availability of beds for inpatients, impacts long waiting times and length of stay (LOS), causing pain and dissatisfaction to patients. Patient length of stay is chosen to be a measure of ED overcrowding as a compliance measure set by most hospitals. Clinicians need to get an opportunity to be proactive in ED overcrowding crises, specifically in the case of peak days. For this purpose, the research aims to build a model to forecast Hajj patient LOS, using machine learning algorithms through predictive input factors such as patient age, mode of arrival, and patient’s type of condition in the ED. Therefore, using machine learning algorithms, such as artificial neural networks, linear and logistic regressions, to forecast ED LOS allows clinicians to prepare for high levels of congestion and provide insights to determine the LOS of patients during vital times.