January 30, 2025Open Access

Forecasting Adolescent Opioid Exposure Risk Using Ensemble Machine Learning: Toward Scalable Early Intervention Models in State Health Systems

AOAdetunji OluwatofunmiBabcock University OOObiajurum Anthonia Odili

Key Points

Ensemble machine learning models significantly enhance opioid exposure risk prediction, allowing better prevention strategies.
The study shows a 20% improvement in prediction accuracy compared to single models, aiding in targeted early interventions.
Employing diverse algorithms like random forests and deep neural networks, this approach captures complex data factors effectively.
The scalable design of these models supports state health systems in resource allocation and risk management.

Abstract

The opioid crisis continues to impose a substantial burden on public health systems, with adolescents representing a particularly vulnerable population due to early exposure risks and long-term dependency outcomes. Effective forecasting of opioid exposure risk requires advanced predictive models capable of addressing the complex interplay of behavioral, clinical, demographic, and socioeconomic factors. This study proposes an ensemble machine learning framework for forecasting adolescent opioid exposure risk, with the aim of advancing scalable early intervention strategies within state health systems. By leveraging integrated datasets including Medicaid claims, electronic health records, and school-based behavioral surveys the approach incorporates multiple algorithms such as random forests, gradient boosting machines, and deep neural networks to enhance predictive accuracy and robustness. The ensemble design allows for improved generalization across heterogeneous subpopulations while addressing class imbalance and temporal variability in opioid prescription trends. The case study demonstrates how ensemble models outperform single learners in sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), ensuring reliable identification of high-risk adolescents. Furthermore, the study outlines a framework for translating predictive insights into policy-driven interventions, including targeted prevention programs, personalized care pathways, and real-time monitoring of prescription practices. Importantly, the scalability of the model positions it as a viable tool for state-level deployment, supporting health systems in resource allocation, risk stratification, and program evaluation. By bridging advanced machine learning with public health imperatives, this research underscores the potential of ensemble forecasting models to mitigate adolescent opioid exposure and strengthen early intervention mechanisms in response to a growing public health crisis.

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