Predicting In-Hospital Mortality at Admission to the Medical Ward: A Big-Data Machine Learning Model.

Journal: The American journal of medicine
Published Date: Aug 15, 2020

Abstract

BACKGROUND: General medical wards admit high-risk patients. Artificial intelligence algorithms can use big data for developing models to assess patients' risk stratification. The aim of this study was to develop a mortality prediction machine learning model using data available at the time of admission to the medical ward.

Authors

  • 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 Klang
    Division of Data-Driven and Digital Medicine (D3M), Icahn School of Medicine at Mount Sinai, New York, NY, USA.
  • Yiftach Barash
    Department of Diagnostic Imaging, Chaim Sheba Medical Center, Tel Hashomer, Israel.
  • Ehud Grossman
    Internal Medicine, Tel-Hashomer, Israel; Sackler Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel.
  • Eyal Zimlichman
    Sheba Medical Center, Tel Hashomer, Israel.

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