Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare.

Journal: Journal of the American Medical Informatics Association : JAMIA
Published Date: Jun 1, 2025

Abstract

OBJECTIVE: Building upon our previous work on predicting chronic opioid use using electronic health records (EHR) and wearable data, this study leveraged the Health Equity Across the AI Lifecycle (HEAAL) framework to (a) fine tune the previously built model with genomic data and evaluate model performance in predicting chronic opioid use and (b) apply IBM's AIF360 pre-processing toolkit to mitigate bias related to gender and race and evaluate the model performance using various fairness metrics.

Authors

  • Nidhi Soley
    Institute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, Baltimore, Maryland, United States.
  • Ilia Rattsev
    Institute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University, Baltimore, MD 21218, United States.
  • Traci J Speed
    Department of Psychiatry and Behavioral Sciences, Johns Hopkins University, School of Medicine, Baltimore, Maryland, United States.
  • Anping Xie
    Armstrong Institute for Patient Safety and Quality, Johns Hopkins University, School of Medicine, Baltimore, Maryland, United States.
  • Kadija S Ferryman
    Department of Health Policy & Management, Johns Hopkins University, Bloomberg School of Public Health, Baltimore, MD 21205, United States.
  • Casey Overby Taylor
    Johns Hopkins Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, United States of America.

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