Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study.

Journal: BMJ open
Published Date: Aug 10, 2019

Abstract

OBJECTIVES: The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy.

Authors

  • Mathias Carl Blom
    Department of Clinical Sciences Lund, Medicine, Lund University, Medical Faculty, Lund, Sweden.
  • Awais Ashfaq
    Center for Applied Intelligent Systems Research, Halmstad University, Sweden; Halland Hospital, Region Halland, Sweden. Electronic address: awais.ashfaq@hh.se.
  • Anita Sant'Anna
    Center for Applied Intelligent Systems Research, Halmstad University, Sweden.
  • Philip D Anderson
    Department of Emergency Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA.
  • Markus Lingman
    Halland Hospital, Region Halland, Sweden; Institute of Medicine, Dept. of Molecular and Clinical Medicine/Cardiology, Sahlgrenska Academy, University of Gothenburg, Sweden.

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