Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning.

Journal: BMC medical informatics and decision making
Published Date:

Abstract

INTRODUCTION: Emergency departments (EDs) are critical for ensuring timely patient care, especially in triage, where accurate prioritisation is essential for patient safety and resource utilisation. Building on previous research, this study leverages a comprehensive dataset of 653,546 ED visits spanning six years from Mater Dei Hospital, Malta. This dataset enables detailed trend analysis, demographic variation exploration, and predictive modelling of patient prioritisation, admission likelihood, and admitting ward.

Authors

  • Steve Agius
    University of Malta, Msida, Malta. stephen.agius@um.edu.mt.
  • Vincent Cassar
    University of Malta, Msida, Malta.
  • Caroline Magri
    University of Malta, Msida, Malta.
  • Wasiq Khan
    Computer Science Department, Liverpool John Moores University, Liverpool L33AF, UK.
  • Dhiya Al-Jumeily Obe
    Liverpool John Moores University, Liverpool, UK.
  • Godwin Caruana
    University of Malta, Msida, Malta.
  • Luke Topham
    Liverpool John Moores University, Liverpool, UK. L.K.Topham@ljmu.ac.uk.