Predicting Unplanned Return to Operating Room Following Primary Total Shoulder Arthroplasty: Insights from Fair and Explainable Ensemble Machine Learning

Authors

Annie Kim, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA, USA.
Hongtao Wang, School of Computing and Information, University of Pittsburgh, PA, USA.
Nicole Myers, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA, USA.
Puneet Gupta, School of Medicine, George Washington University, DC, USA.
Fritz Steuer, School of Medicine, University of Pittsburgh, PA, USA.
Michael R. Kann, School of Medicine, University of Pittsburgh, PA, USA.
Ting Cong, University of Pittsburgh Medical Center, PA, USA.
Hongfang Liu, University of Texas Health Science Center at Houston, TX, USA.
Ahmad P. Tafti, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA, USA.

Document Type

Journal Article

Publication Date

9-24-2024

Journal

Studies in health technology and informatics

Volume

318

DOI

10.3233/SHTI240908

Keywords

TSA; fair and explainable machine learning; total shoulder arthroplasty

Abstract

Reoperation is the most significant complication following any surgical procedure. Developing machine learning methods that predict the need for reoperation will allow for improved shared surgical decision making and patient-specific and preoperative optimisation. Yet, no precise machine learning models have been published to perform well in predicting the need for reoperation within 30 days following primary total shoulder arthroplasty (TSA). This study aimed to build, train, and evaluate a fair (unbiased) and explainable ensemble machine learning method that predicts return to the operating room following primary TSA with an accuracy of 0.852 and AUC of 0.91.

APA Citation

Kim, Annie; Wang, Hongtao; Myers, Nicole; Gupta, Puneet; Steuer, Fritz; Kann, Michael R.; Cong, Ting; Liu, Hongfang; and Tafti, Ahmad P., "Predicting Unplanned Return to Operating Room Following Primary Total Shoulder Arthroplasty: Insights from Fair and Explainable Ensemble Machine Learning" (2024). GW Authored Works. Paper 5594.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/5594

Department

School of Medicine and Health Sciences Resident Works