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Explainable machine learning for predicting opioid-related aberrant behavior: A multimodal approach using clinical

Mubashir Farooq1, Asif Ali Banka1

  • 1Department of Computer Science and Engineering, Islamic University of Science and Technology, Kashmir, Jammu and Kashmir 192122, India.

Drug and Alcohol Dependence
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Summary

This study developed an AI model to predict opioid misuse early. The explainable AI framework accurately identifies patients at risk for opioid-related aberrant behaviors, aiding safe opioid management.

Keywords:
Addictive BehaviorClinical Text AnalysisExplainable AIMultimodal LearningOpioid Aberrant BehaviorSHAP Interpretability

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Area of Science:

  • Artificial Intelligence in Healthcare
  • Machine Learning for Clinical Decision Support
  • Opioid Use Disorder Research

Background:

  • The opioid epidemic remains a critical public health issue, exacerbated by chronic pain treatment and rising overdose deaths.
  • Opioid-related aberrant behaviors (ORABs) are early indicators of potential opioid misuse, necessitating advanced predictive tools.
  • Current risk assessment models often lack the precision and explainability required for effective clinical implementation.

Purpose of the Study:

  • To develop and validate an explainable machine learning framework for predicting confirmed ORABs.
  • To integrate diverse data sources, including clinical text and structured electronic health record (EHR) data, for enhanced predictive accuracy.
  • To provide clinically actionable insights into the factors contributing to ORABs.

Main Methods:

  • A multimodal approach combining GloVe and ClinicalBERT embeddings for EHR text analysis.
  • Implementation of Synthetic Minority Oversampling Technique (SMOTE) for addressing data imbalance.
  • Training and evaluation of various machine learning algorithms, including ensemble methods and neural networks, with SHAP for explainability.

Main Results:

  • The Opioid Risk Ensemble model achieved 96.0% AUROC and 98.8% accuracy.
  • The Opioid Risk Neural Network, utilizing ClinicalBERT, demonstrated 98.75% AUROC and 98.47% accuracy.
  • SHAP analysis identified opioid and benzodiazepine prescriptions, alongside CNS-related factors, as significant predictors, with key clinical note terms also highlighted.

Conclusions:

  • The developed explainable AI framework offers a valuable tool for modern healthcare decision-making.
  • This approach enhances the ability to predict ORABs, supporting safer opioid management strategies.
  • Multimodal, explainable AI holds significant promise for improving patient safety and mitigating the impact of the opioid epidemic.