The Sydney Triage to Admission Risk Tool (START2) using machine learning techniques to support disposition decision-making.

Journal: Emergency medicine Australasia : EMA
PMID: 30469164

Abstract

OBJECTIVE: To further develop and refine an Emergency Department (ED) in-patient admission prediction model using machine learning techniques.

Authors

  • Kathryn Rendell
    School of Aerospace, Mechanical and Mechatronic Engineering, Faculty of Engineering and Information Technologies, The University of Sydney, Sydney, New South Wales, Australia.
  • Irena Koprinska
    School of Information Technologies, University of Sydney, Australia.
  • Andre Kyme
    School of Aerospace, Mechanical and Mechatronic Engineering, Faculty of Engineering and Information Technologies, The University of Sydney, Sydney, New South Wales, Australia.
  • Anja A Ebker-White
    Emergency Department, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia.
  • Michael M Dinh
    Emergency Department, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia.

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