Optimal inputs for machine learning models in predicting total joint arthroplasty outcomes: a systematic review.

Journal: European journal of orthopaedic surgery & traumatology : orthopedie traumatologie
PMID: 39212689

Abstract

INTRODUCTION: Machine learning (ML) models may offer a novel solution to reducing postoperative complication rates and improving post-surgical outcomes after total joint arthroplasty (TJA). However, the variety of different ML models that exist paired with the increasing number of potential inputs can make the implementation of this tool challenging. Therefore, we conducted a systematic review to assess the most optimal inputs of different ML models in predicting postoperative (1) medical outcomes, (2) orthopedic outcomes, and (3) patient-reported outcome measures (PROMs) after total joint arthroplasty.

Authors

  • Parshva A Sanghvi
    Department of Orthopaedic Surgery, Center for Hip Preservation, Orthopaedic and Rheumatologic Institute, Institute Cleveland Clinic Foundation, Mail code A41, 9500 Euclid Ave, Cleveland, OH, 44195, USA.
  • Aakash K Shah
    Department of Orthopaedic Surgery, Center for Hip Preservation, Orthopaedic and Rheumatologic Institute, Institute Cleveland Clinic Foundation, Mail code A41, 9500 Euclid Ave, Cleveland, OH, 44195, USA.
  • Christian J Hecht
    Department of Orthopaedic Surgery, Cleveland Clinic Foundation, 9500 Euclid Ave, Cleveland, OH, 44195, USA.
  • Amir H Karimi
    Department of Orthopaedic Surgery, Center for Hip Preservation, Orthopaedic and Rheumatologic Institute, Institute Cleveland Clinic Foundation, Mail code A41, 9500 Euclid Ave, Cleveland, OH, 44195, USA.
  • Atul F Kamath
    Machine Learning Arthroplasty Lab, Cleveland Clinic, Cleveland, OH.

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