A Critical Integrative Review of Machine Learning Approaches for Predicting Opioid Overdose Risk

Godwin Tetteh Wayoe; Godson Teye Apaflo

doi:10.36948/ijfmr.2026.v08i04.83882

A Critical Integrative Review of Machine Learning Approaches for Predicting Opioid Overdose Risk

Author(s)	Mr. Godwin Tetteh Wayoe, Mr. Godson Teye Apaflo
Country	United States
Abstract	Abstract Background: Machine learning has been increasingly applied to predict opioid overdose risk, yet the extent to which these models can support clinical decision-making and population-level overdose prevention remains unclear. Objective: This critical integrative review synthesizes evidence on machine learning approaches for predicting opioid overdose risk. Studies were synthesized using a critical integrative approach emphasizing comparative evaluation of model types, predictors, validation practices, and implementation considerations. Methods: Studies applying machine learning to predict opioid overdose, opioid use disorder, or related outcomes were identified. Twelve primary modeling studies were included and analyzed using a critical integrative review approach focused on comparative interpretation and identification of structural patterns across the literature. Results: Tree-based ensemble methods frequently demonstrated moderate to high discrimination, with reported AUC values ranging from 0.72 to 0.79 in studies that provided this metric. Ecological and surveillance-enhanced models (particularly those incorporating unstructured emergency medical services data) showed value for geographic risk targeting. Key predictors included prescription patterns, mental health and substance use history, and prior overdose events. However, external validation was limited, fairness considerations were underdeveloped, and evidence of real-world implementation and impact remained scarce. Conclusions: Although machine learning models can identify individuals and populations at elevated overdose risk, a substantial gap persists between technical development and effective translation into practice. Greater attention to external validation, fairness, implementation science, and multi-level integration is needed to realize the potential of these tools for overdose prevention.
Keywords	machine learning, opioid overdose, risk prediction, external validation, fairness
Field	Computer > Data / Information
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-18
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.83882

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A Critical Integrative Review of Machine Learning Approaches for Predicting Opioid Overdose Risk

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