Common Challenges in Predicting Opioid-related Outcomes Using Machine Learning

Machine learning (ML) models have been commonly utilized to predict various opioid-related outcomes and risks, including post-operative opioid use, opioid use disorder (OUD), misuse, or overdose. Despite their promising performance, the clinical utility and cross-study comparability of ML models are constrained. This is mainly due to variability in outcome definitions, data heterogeneity, class imbalance and analysis concerns, inadequate external or prospective validation, and limited real-world deployment and ethical concerns. Herein, we present an updated overview of these challenges in predicting opioid-related outcomes and outline strategies to improve the clinically meaningful applications of ML in this domain.