Utilizing Machine Learning for Early Intervention and Risk Management in the Opioid Overdose Crisis

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ABSTRACTThis systematic review and meta‐analysis seek to identify prevalent machine learning (ML) models applied to outcomes related to illicit opioid use. Following PRISMA guidelines, we reviewed databases including MEDLINE, Embase, CINAHL, PsycINFO, and Web of Science, yielding 10,666 records. Of these, 6029 were unique, leading to 155 full‐text publications, with 69 studies meeting inclusion criteria. The inclusion criteria focused on two primary themes: the application of artificial intelligence and machine learning techniques, and opioid related substance use outcomes. The meta‐analysis focused on Area Under the Receiver Operating Characteristic curve (AUC/AUROC). Most of the studies used classification models and evaluated them using the AUC metric. Cohen's d effect sizes were 1.22 for logistic regression (AUC = 0.806), 1.26 for decision trees/random forests (AUC = 0.814), 1.54 for deep learning (AUC = 0.862), and 1.27 for boosting algorithms (AUC = 0.815). Regarding outcomes, effect sizes were 1.42 for opioid use disorder (OUD) (AUC = 0.842), 1.37 for opioid overdoses (AUC = 0.842), and 1.25 for risk of drug use (AUC = 0.812). The study reveals the efficacy of ML in illicit opioid use, with a notable predominance of supervised ML models, particularly Logistic Regression. The underutilization of regression models, despite their potential in outcome quantification, was surprising. Deep learning emerged as the most effective model, demonstrating the complexity of data in addiction psychiatry. ML algorithms provide a powerful framework for informed decision‐making in addiction care, leading toward personalized medicine and reducing unregulated drug use and related harms.