A Novel Machine Learning Model Developed to Assist in Patient Selection for Outpatient Total Shoulder Arthroplasty.

Journal: The Journal of the American Academy of Orthopaedic Surgeons
Published Date: Jul 1, 2020

Abstract

INTRODUCTION: Patient selection for outpatient total shoulder arthroplasty (TSA) is important to optimizing patient outcomes. This study aims to develop a machine learning tool that may aid in patient selection for outpatient total should arthroplasty based on medical comorbidities and demographic factors.

Authors

  • Dustin R Biron
    From the Warren Alpert Medical School of Brown University (Biron, Sinha, Dr. Kleiner, and Aluthge), Center for Biomedical Informatics (Biron, Sinha, Aluthge, and Dr. Sarkar), Brown University, and Department of Orthopaedic Surgery (Dr. Goodman, Dr. Cohen, and Dr. Daniels), The Warren Alpert Medical School of Brown University, Rhode Island Hospital, Providence, RI.
  • Ishan Sinha
    Warren Alpert Medical School of Brown University, Providence, Rhode Island; Brown Center for Biomedical Informatics, Brown University, 233 Richmond Street, Box G-R, Providence, RI 02912. Electronic address: ishan_sinha@brown.edu.
  • Justin E Kleiner
  • Dilum P Aluthge
    Warren Alpert Medical School of Brown University, Providence, Rhode Island; Brown Center for Biomedical Informatics, Brown University, 233 Richmond Street, Box G-R, Providence, RI 02912.
  • Avi D Goodman
  • I Neil Sarkar
  • Eric Cohen
  • Alan H Daniels
    1Division of Spine Surgery and.

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