Machine learning techniques for mortality prediction in emergency departments: a systematic review.

Journal: BMJ open
Published Date: Nov 2, 2021

Abstract

OBJECTIVES: This systematic review aimed to assess the performance and clinical feasibility of machine learning (ML) algorithms in prediction of in-hospital mortality for medical patients using vital signs at emergency departments (EDs).

Authors

  • Amin Naemi
    Centre of Health Informatics and Technology, The Maersk Mc-Kinney Moller, Institute, University of Southern Denmark, Odense, Denmark.
  • Thomas Schmidt
    Department for Clinical Research, Schüchtermann-Klinik Bad Rothenfelde, Germany.
  • Marjan Mansourvar
    Centre of Health Informatics and Technology, The Maersk Mc-Kinney Moller, Institute, University of Southern Denmark, Odense, Denmark.
  • Mohammad Naghavi-Behzad
    Department of Clinical Research, University of Southern Denmark, Odense, Denmark.
  • Ali Ebrahimi
    Laboratory for Computational Sensing and Robotics, Johns Hopkins University, Baltimore, MD.
  • Uffe Kock Wiil
    Maersk Mc-Kinney Moller Institute, University of Southern Denmark, Denmark.

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