Random forest identifies predictors of discharge destination following total shoulder arthroplasty.

Journal: JSES international
Published Date: May 12, 2023

Abstract

BACKGROUND: Machine learning algorithms are finding increasing use in prediction of surgical outcomes in orthopedics. Random forest is one of such algorithms popular for its relative ease of application and high predictability. In the process of sample classification, algorithms also generate a list of variables most crucial in the sorting process. Total shoulder arthroplasty (TSA) is a common orthopedic procedure after which most patients are discharged home. The authors hypothesized that random forest algorithm would be able to determine most important variables in prediction of nonhome discharge.

Authors

  • Jun Ho Chung
    Loma Linda University, Loma Linda, CA, USA.
  • Damien Cannon
    Loma Linda University, Loma Linda, CA, USA.
  • Matthew Gulbrandsen
    Loma Linda University, Loma Linda, CA, USA.
  • Dheeraj Yalamanchili
    Loma Linda University, Loma Linda, CA, USA.
  • Wesley P Phipatanakul
    Loma Linda University, Loma Linda, CA, USA.
  • Joseph Liu
    University of Southern California, Los Angeles, CA, USA.
  • Anirudh Gowd
    Wake Forest Univesrsity, Winton-Salem, NC, USA.
  • Anthony Essilfie
    Loma Linda University, Loma Linda, CA, USA.

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