A Comparative Effectiveness Study on Opioid Use Disorder Prediction Using Artificial Intelligence and Existing Risk Models.

Abstract

Opioid use disorder (OUD) is a leading cause of death in the United States placing a tremendous burden on patients, their families, and health care systems. Artificial intelligence (AI) can be harnessed with available healthcare data to produce automated OUD prediction tools. In this retrospective study, we developed AI based models for OUD prediction and showed that AI can predict OUD more effectively than existing clinical tools including the unweighted opioid risk tool (ORT). Data include 474,208 patients' data over 10 years; 269,748 were females with an average age of 56.78 years. Cases are prescription opioid users with at least one diagnosis of OUD or at least one prescription for buprenorphine or methadone. Controls are prescription opioid users with no OUD diagnoses or buprenorphine or methadone prescriptions. On 100 randomly selected test sets including 47,396 patients, our proposed transformer-based AI model can predict OUD more efficiently (AUC=0.742 ±0.021) compared to logistic regression (AUC=0.651 ±0.025), random forest (AUC=0.679 ±0.026), xgboost (AUC=0.690 ±0.027), long short-term memory model (AUC=0.706 ±0.026), transformer (AUC=0.725 ±0.024), and unweighted ORT model (AUC=0.559 ±0.025). Our results show that embedding AI algorithms into clinical care may assist clinicians in risk stratification and management of patients receiving opioid therapy.