Developing interpretable machine learning models to predict length of stay and disposition decision for adult patients in emergency departments.

Journal: BMJ health & care informatics
Published Date: Jun 26, 2025

Abstract

OBJECTIVE: Machine learning (ML) models have emerged as tools to predict length of stay (LOS) and disposition decision (DD) in emergency departments (EDs) to combat overcrowding. However, site-specific ML models are not transferable to different sites. Our objective was to develop interpretable ML models to predict LOS and DD at specific time points, all while establishing a transparent data analysis framework. This framework was designed to be easily adapted by other institutions for the development of their own ML models.

Authors

  • Long Song
    School of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia.
  • Uwe Aickelin
    School of Computing and Information Systems, The University of Melbourne, Melbourne 3010, Australia.
  • Timothy N Fazio
    EMR Team, The Royal Melbourne Hospital, Parkville, Victoria, Australia.
  • Abhishek Sharma
    Department of Biotechnology and Bioengineering, Institute of Advanced Research, Koba Institutional Area, Gandhinagar, India.
  • Mojgan Kouhounestani
    School of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia.
  • Samantha Plumb
    The Royal Melbourne Hospital, Parkville, Victoria, Australia.
  • Mark John Putland
    Department of Emergency Medicine, The Royal Melbourne Hospital, Parkville, Victoria, Australia Mark.Putland@mh.org.au.

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