Machine Learning Algorithms Exceed Comorbidity Indices in Prediction of Short-Term Complications After Hip Fracture Surgery.

Journal: The Journal of the American Academy of Orthopaedic Surgeons
Published Date: Nov 19, 2024

Abstract

BACKGROUND: Hip fractures are among the most morbid acute orthopaedic injuries often due to accompanying patient frailty. The purpose of this study was to determine the reliability of assessing surgical risk after hip fracture through machine learning (ML) algorithms.

Authors

  • Anirudh K Gowd
    Wake Forest University Baptist Medical Center, Winston-Salem, NC, USA. Electronic address: anirudhkgowd@gmail.com.
  • Edward C Beck
    Division of Sports Medicine, Department of Orthopedic Surgery, Wake Forest Baptist Health, Winston-Salem, North Carolina, USA.
  • Avinesh Agarwalla
    Westchester Medical Center, Valhalla, NY, USA.
  • Dev M Patel
    Department of Health Policy and Management, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
  • Ryan C Godwin
    Department of Anesthesiology and Perioperative Medicine, University of Alabama at Birmingham, Birmingham, AL, United States of America.
  • Brian R Waterman
    Division of Sports Medicine, Department of Orthopedic Surgery, Wake Forest Baptist Health, Winston-Salem, North Carolina, USA.
  • Milton T Little
  • Joseph N Liu
    Loma Linda University Medical Center, Loma Linda, CA, USA.

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