Development and Temporal Evaluation of Multimodal Machine Learning Models to Predict High Inpatient Opioid Exposure

High inpatient opioid exposure is associated with increased risk of persistent opioid use. Early identification of high-risk patients may improve opioid stewardship. We developed machine learning models to predict high opioid exposure during hospitalization using electronic health record data from MIMIC-IV. We conducted a retrospective study of 223,452 unique first hospital admissions in MIMIC-IV. The outcome was high opioid exposure, defined as the top decile among opioid-exposed admissions (MME/day [≥] 225), representing 2.65% of all admissions. Structured early-admission features included demographics, admission characteristics, laboratory utilization and abnormality summaries, and 24-hour procedural indicators. Discharge-note data were incorporated using ClinicalBERT embeddings and interpretable bigram features. Models were trained using an 80/10/10 split and evaluated with temporal validation on the most recent 10% of admissions. Performance was assessed using ROC-AUC and PR-AUC with 95% confidence intervals. Among structured-only models, XGBoost achieved the best test performance (ROC-AUC 0.932 [0.924-0.940]; PR-AUC 0.223 [0.193-0.262]). The combined structured and notes model improved precision-recall performance (ROC-AUC 0.932 [0.920-0.943]; PR-AUC 0.276 [0.229-0.331]). Temporal evaluation showed similar discrimination (ROC-AUC 0.929; PR-AUC 0.223). High-risk bigrams included procedural terms such as "external fixation" and "cervical discectomy." Integration of structured and text-derived features improved risk stratification compared to structured data alone. Interpretable bigram signals reflected procedural complexity and orthopedic pathology, reinforcing the clinical plausibility of model predictions. Multimodal EHR-based models accurately predict high inpatient opioid exposure and may support targeted opioid stewardship during hospitalization.