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We fine-tuned 4 machine learning models on the new dataset. The SHapley Additive exPlanations (SHAP) technique identified the best-performing predictors. A preprocessing toolkit boosted fairness by gender and race.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The genetic data enhanced model performance from the prior model, with the area under the curve improving from 0.90 (95% CI, 0.88-0.92) to 0.95 (95% CI, 0.89-0.95). Key predictors included Dopamine D1 Receptor (DRD1) rs4532, general type of surgery, and time spent in physical activity. The reweighing preprocessing technique applied to the stacking algorithm effectively improved the model\u2019s fairness across racial and gender groups without compromising performance.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusion<\/jats:title>\n                  <jats:p>We leveraged 2 dimensions of the HEAAL framework to build a fair artificial intelligence (AI) solution. Multi-modal datasets (including wearable and genetic data) and applying bias mitigation strategies can help models to more fairly and accurately assess risk across diverse populations, promoting fairness in AI in healthcare.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/jamia\/ocaf053","type":"journal-article","created":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T17:02:38Z","timestamp":1743440558000},"page":"985-997","source":"Crossref","is-referenced-by-count":5,"title":["Predicting postoperative chronic opioid use with fair machine learning models integrating multi-modal data sources: a demonstration of ethical machine learning in healthcare"],"prefix":"10.1093","volume":"32","author":[{"given":"Nidhi","family":"Soley","sequence":"first","affiliation":[{"name":"Institute for Computational Medicine, Whiting School of Engineering, Johns Hopkins University , Baltimore, MD 21218,","place":["United States"]},{"name":"Department of Biomedical Engineering, Johns Hopkins 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