Explainable machine learning to predict prolonged post-operative opioid use in rotator cuff patients

Abstract

BACKGROUND: Opioid overuse is a costly and significant problem in the United States. Medical specialties including surgery are a contributor to opioid prescriptions while having few clear prescribing guidelines. Machine learning predictive tools can assist surgeons with evaluating patients for opioid prescriptions and help prevent prescriptions for patients at-risk for prolonged post-operative opioid use. This project aims to develop accurate and transparent machine learning models to predict prolonged opioid use by individuals undergoing rotator cuff surgery. METHODS: Six machine learning models were trained on a dataset of 852 individuals undergoing rotator cuff surgery and evaluated for predictive accuracy. Machine learning explainability techniques were used to improve model transparency, including Shapley Additive explanations (SHAP) for global variable importance and local interpretable model-agnostic explanations (LIME) for local variable effects and importance. RESULTS: Four of six machine learning models developed had predictive accuracy greater than 0.71, with our top three models having an accuracy of 0.98 (XGBoost), 0.94 (Random Forest), and 0.74 (Decision Tree). SHAP and LIME explanations were created for models allowing insight and interpretation into model outputs. CONCLUSION: We were able to develop predictive machine learning models that displayed predictive accuracy in predicting prolonged post-operative opioid use while improving model explainability and transparency.

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