What Is the Accuracy of Three Different Machine Learning Techniques to Predict Clinical Outcomes After Shoulder Arthroplasty?

Journal: Clinical orthopaedics and related research
PMID: 32332242

Abstract

BACKGROUND: Machine learning techniques can identify complex relationships in large healthcare datasets and build prediction models that better inform physicians in ways that can assist in patient treatment decision-making. In the domain of shoulder arthroplasty, machine learning appears to have the potential to anticipate patients' results after surgery, but this has not been well explored.

Authors

  • Vikas Kumar
    Department of Urology, King George's Medical University, Lucknow, Uttar Pradesh, India.
  • Christopher Roche
    C. Roche, Exactech, Gainesville, FL, USA.
  • Steven Overman
    V. Kumar, S. Overman, A. Teredesai, KenSci, Seattle, WA, USA.
  • Ryan Simovitch
    R. Simovitch, Hospital For Special Surgery - FL, West Palm Beach, FL 33401, USA.
  • Pierre-Henri Flurin
    P-H. Flurin, Bordeaux-Merignac Sport Clinic, Merignac, France.
  • Thomas Wright
    T. Wright, University of Florida Department of Orthopaedic Surgery, Gainesville, FL, USA.
  • Joseph Zuckerman
    J. Zuckerman, Department of Orthopedic Surgery, NYU Langone Orthopedic Hospital, New York, NY, USA.
  • Howard Routman
    H. Routman, Atlantis Orthopedics, Palm Beach Gardens, FL, USA.
  • Ankur Teredesai
    KenSci Inc., Seattle, Washington, United States.

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