Early prediction of intensive care unit admission in emergency department patients using machine learning.

Journal: Australian critical care : official journal of the Confederation of Australian Critical Care Nurses
PMID: 39643547

Abstract

BACKGROUND: The timely identification and transfer of critically ill patients from the emergency department (ED) to the intensive care unit (ICU) is important for patient care and ED workflow practices.

Authors

  • Dinesh Pandey
    Data Analytics Research and Evaluation (DARE) Centre, The University of Melbourne and Austin Hospital, Melbourne, VIC, Australia; Clinical Analytics and Reporting, Performance Reporting and Decision Support, Austin Health, Melbourne, VIC, Australia.
  • Hossein Jahanabadi
    Data Analytics Research and Evaluation (DARE) Centre, The University of Melbourne and Austin Hospital, Melbourne, VIC, Australia; Clinical Analytics and Reporting, Performance Reporting and Decision Support, Austin Health, Melbourne, VIC, Australia.
  • Jack D'Arcy
    Department of Intensive Care, Austin Health, Heidelberg, Victoria, Australia.
  • Suzanne Doherty
    Department of Emergency Medicine, Austin Health, Heidelberg, Victoria, Australia.
  • Hung Vo
    Data Analytics Research and Evaluation (DARE) Centre, The University of Melbourne and Austin Hospital, Melbourne, VIC, Australia.
  • Daryl Jones
    Australian and New Zealand Intensive Care Research Center, School of Public Health and Preventive Medicine, Monash University, Melbourne, Australia.
  • Rinaldo Bellomo
    Australian and New Zealand Intensive Care Research Centre, Monash University, Melbourne, VIC, Australia.

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