Fairness of machine learning readmission predictions following open ventral hernia repair.

Journal: Surgical endoscopy
Published Date: Jul 7, 2025

Abstract

INTRODUCTION: Few models have predicted readmission following open ventral hernia repair (VHR), and none have assessed fairness. Fairness evaluation assesses whether predictive performance is similar across demographic groups, ensuring that biases are not propagated. Therefore, we generated an interpretable machine learning model to predict readmission following open VHR while assessing fairness.

Authors

  • Tyler Zander
    Department of Surgery, OnetoMap Analytics, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Melissa A Kendall
    Department of Surgery, OnetoMap Analytics, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Rachel L Wolansky
    Department of Surgery, OnetoMap Analytics, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Emily A Grimsley
    Department of Surgery, University of South Florida Morsani College of Medicine, 2 Tampa General Circle, Rm 7015, Tampa, FL, 33606, USA.
  • Rajavi Parikh
    Department of Surgery, OnetoMap Analytics, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Joseph Sujka
    Department of Surgery, University of South Florida Morsani College of Medicine, Tampa, FL, USA.
  • Paul C Kuo
    Loyola University Medical Center, Department of Surgery, 2160 S. 1st Avenue, Maywood, IL 60153, USA; One:MAP Section of Surgical Analytics, Department of Surgery, Loyola University Chicago, 2160 S. 1st Avenue, Maywood, IL 60153, USA. Electronic address: paul.kuo@luhs.org.

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