Machine Learning in the Prediction of Trauma Outcomes: A Systematic Review.

Journal: Annals of emergency medicine
Published Date: Jul 13, 2022

Abstract

STUDY OBJECTIVE: Machine learning models carry unique potential as decision-making aids and prediction tools for improving patient care. Traumatically injured patients provide a uniquely heterogeneous population with severe injuries that can be difficult to predict. Given the relative infancy of machine learning applications in medicine, this systematic review aimed to better understand the current state of machine learning development and implementation to help create a basis for future research.

Authors

  • Timothy Zhang
    Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. Electronic address: timothymed.zhang@mail.utoronto.ca.
  • Anton Nikouline
    Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, Ontario, Canada.
  • David Lightfoot
    Health Science Library, Unity Health Toronto, St. Michael's Hospital, Toronto, Ontario, Canada.
  • Brodie Nolan
    Division of Emergency Medicine, Department of Medicine, University of Toronto, Toronto, Ontario, Canada; Li Ka Shing Knowledge Institute, St. Michael's Hospital, Toronto, Ontario, Canada; Department of Emergency Medicine, St. Michael's Hospital, Toronto, Ontario, Canada.

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