A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score.

Journal: Journal of general internal medicine
Published Date: Nov 1, 2019

Abstract

BACKGROUND: Emergency departments (ED) are becoming increasingly overwhelmed, increasing poor outcomes. Triage scores aim to optimize the waiting time and prioritize the resource usage. Artificial intelligence (AI) algorithms offer advantages for creating predictive clinical applications.

Authors

  • Maximiliano Klug
    Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel.
  • Yiftach Barash
    Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel.
  • Sigalit Bechler
    Intuit, HaHarash St.4, Building C 2nd floor, 4524075, Hod HaSharon, Israel.
  • Yehezkel S Resheff
    Intuit, HaHarash St.4, Building C 2nd floor, 4524075, Hod HaSharon, Israel.
  • Talia Tron
    Intuit Israel©, Hod Hasharon, Israel.
  • Avi Ironi
    Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Shelly Soffer
    From the Department of Diagnostic Imaging, Sheba Medical Center, Emek HaEla St 1, Ramat Gan, Israel (S.S., M.M.A., E.K.); Faculty of Engineering, Department of Biomedical Engineering, Medical Image Processing Laboratory, Tel Aviv University, Tel Aviv, Israel (A.B., H.G.); and Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel (S.S., O.S.).
  • Eyal Zimlichman
    Sheba Medical Center, Tel Hashomer, Israel.
  • Eyal Klang
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.

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