A Machine Learning Framework for Predicting the Risk of Opioid Overdose and Hospital Management: A Literature Review

Abstract

Opioid use disorder (OUD), hospitalizations, and mortality have?been rising internationally; the ongoing opioid epidemic is now a worldwide public health issue. Predictive modeling using ML presents an opportunity to identify populations at risk?and to promote public health response. To predict LOS and potential opioid abuse/dependence among overdose patients in?the hospital, this paper presents a holistic machine learning model that fuses structured and unstructured clinical features. We summarise some recent work using various ML methods (such as Random Forest, K-Means, Gradient Boosting, Logistic Regression, Mini-Batch K-Means, deep-learning models)?focusing on different datasets, including MIMIC-III and electronic health records (EHRs). Adding risk factor evaluations,?additional patient demographic features, and historical trends into model releases improves interpretability and model stability.

Introduction

Opioid overdoses are the leading cause of drug-induced deaths globally and represent a major public health crisis. Since 1999, opioids—both prescribed and illicit—have caused over 800,000 deaths in the U.S., with synthetic opioids like fentanyl significantly worsening the problem, especially in underserved communities.

Complex Nature of Opioid Use Disorder (OUD):
OUD is influenced by biological, psychological, and social factors. Patients often suffer from chronic pain, mental illness, and socioeconomic instability. Traditional clinical approaches alone are insufficient. Healthcare systems need smarter tools to predict risks and allocate resources effectively.

Role of Machine Learning and Electronic Health Records (EHR):
Machine learning (ML) models applied to structured (e.g., medication history) and unstructured (e.g., doctor’s notes) EHR data can:

• Predict opioid overdose risk and hospital length of stay (LOS)

• Enable early intervention and personalized treatment plans

• Help hospitals optimize resource use and lower healthcare costs

Challenges in Implementation:

• Integrating ML into legacy EHR systems requires strong APIs and real-time processing capabilities

• Requires collaboration among clinicians, data scientists, and IT teams

• Ethical issues include ensuring patient privacy, managing algorithm bias, and maintaining transparency

• Explainable AI and consent frameworks are vital for patient trust and compliance with regulations like HIPAA

Policy and Public Health Implications:

• Predictive tools align with national goals to reduce opioid-related deaths

• Can inform public health officials about emerging hotspots and intervention needs

• Data sharing between healthcare and public health systems can improve outcomes, provided privacy is maintained

• Funding and operational support are essential for large-scale deployment

Machine Learning Techniques Used:

• Traditional models: Logistic Regression, Random Forests, SVM, Decision Trees — easy to interpret and effective with structured data

• Advanced models: Gradient Boosting (e.g., XGBoost, LightGBM) — high accuracy and good with large datasets

• Deep learning models: CNNs, RNNs, and LSTM — well-suited for text data (e.g., clinical notes) and identifying complex patterns over time

Model Comparison:

• Studies compare general ML models and deep learning approaches to find a balance between accuracy, interpretability, and adaptability.

• LSTM models show promise in extracting patterns like relapse risk and medication tapering from clinical narratives.

Conclusion

One promising approach to solving the opioid crisis is machine learning. Predictive models can forecast the areas in which a patient is at risk and resource requirements by utilizing a variety of clinical data sources. By incorporating these models into hospital procedures, the strain of opioid abuse on the healthcare system can be lessened, patient care can be improved, and healthcare operations can be optimized. Future studies should examine real-time clinical alert systems that adjust to patient status updates in dynamic environments, federated learning for cross-institution collaboration, and integration with behavioral health analytics. The quality and responsiveness of patient care could be improved by Current hospital workflow incorporating machine learning models. Clinical decision support systems (CDSS) with predictive alerts built in can instantly alert clinicians to overdose risk (Mukherjee et al., 2020) (Rajkomar et al., 2019), allowing for early intervention and individualized treatment plans. Additionally, in hospital the length of stay (LOS) risk prediction can help with capacity management (Wu et al., 2025) and discharge planning, improving system efficiency overall.

References

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Copyright

Copyright © 2025 Mr. Guruprasad K M, Karan P Gowda, Devicharan Reddy, Kiran G R, Mourya Jagadeesh. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.