Machine Learning and Surgical Outcomes Prediction: A Systematic Review.

Journal: The Journal of surgical research
Published Date: Aug 1, 2021

Abstract

BACKGROUND: Machine learning (ML) has garnered increasing attention as a means to quantitatively analyze the growing and complex medical data to improve individualized patient care. We herein aim to critically examine the current state of ML in predicting surgical outcomes, evaluate the quality of currently available research, and propose areas of improvement for future uses of ML in surgery.

Authors

  • Omar Elfanagely
    Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania. Electronic address: oelfanagely@gmail.com.
  • Yoshiko Toyoda
    Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Sammy Othman
    Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Joseph A Mellia
    Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania.
  • Marten Basta
    Department of Plastic and Reconstructive Surgery, Brown University, Providence, Rhode Island.
  • Tony Liu
    University of Pennsylvania, USA.
  • Konrad Kording
    Laura Prosser, PhD, PTR is a Assistant Professor of Pediatrics, the Perelman School of Medicine, University of Pennsylvania and a physical therapist, Children's Hospital of Philadelphia.
  • Lyle Ungar
    University of Pennsylvania, USA.
  • John P Fischer
    Division of Plastic Surgery, Department of Surgery, University of Pennsylvania, Philadelphia, Pennsylvania.

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