Decision analysis and reinforcement learning in surgical decision-making.

Journal: Surgery
Published Date: Aug 1, 2020

Abstract

BACKGROUND: Surgical patients incur preventable harm from cognitive and judgment errors made under time constraints and uncertainty regarding patients' diagnoses and predicted response to treatment. Decision analysis and techniques of reinforcement learning theoretically can mitigate these challenges but are poorly understood and rarely used clinically. This review seeks to promote an understanding of decision analysis and reinforcement learning by describing their use in the context of surgical decision-making.

Authors

  • Tyler J Loftus
    Department of Surgery, University of Florida Health, Gainesville, FL. Electronic address: tyler.loftus@surgery.ufl.edu.
  • Amanda C Filiberto
    Department of Surgery, University of Florida Health, Gainesville, FL, USA.
  • Yanjun Li
    NSF Center for Big Learning, University of Florida, Gainesville, FL.
  • Jeremy Balch
    Department of Surgery, University of Florida Health, Gainesville, FL, USA.
  • Allyson C Cook
    Department of Medicine, University of California, San Francisco, CA.
  • Patrick J Tighe
    Department of Anesthesiology, University of Florida College of Medicine, Gainesville, Florida, USA.
  • Philip A Efron
    Department of Surgery, Sepsis and Critical Illness Research Center, University of Florida Health, Gainesville, Florida.
  • Gilbert R Upchurch
    TCV Division, Department of Surgery, University of Virginia Medical Center, Charlottesville, Virginia.
  • Parisa Rashidi
    Department of Biomedical Engineering, University of Florida, Gainesville, FL USA.
  • Xiaolin Li
    National Science Foundation Center for Big Learning, University of Florida, Gainesville, FL 32611, USA.
  • Azra Bihorac
    Department of Medicine, University of Florida, Gainesville, FL USA.

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