Explainable Machine Learning for Predicting Adverse Drug Events in Older Adults with Polypharmacy: A Single-Center Retrospective Cohort Study

Background: Polypharmacy is associated with increased adverse drug event (ADE) risk in older adults, but accurate risk stratification remains challenging. This study aimed to develop and evaluate explainable machine learning (ML) models for predicting ADEs in older adults with polypharmacy. Methods: This single-center retrospective cohort study included adults aged ≥65 years who received outpatient care at Daegu Catholic University Medical Center between January 2016 and December 2025. Logistic regression, random forest, and Light Gradient-Boosting Machine (LightGBM) models were developed using demographic, comorbidity, medication, and laboratory variables. Model performance was evaluated using discrimination, calibration, and classification metrics. SHapley Additive exPlanations (SHAP) analyses were performed to improve model interpretability. Results: A total of 7505 older adults were included, including 366 patients who developed ADEs within 90 days. In the independent test set, random forest demonstrated favorable overall classification performance, achieving the highest accuracy (0.810), specificity (0.828), and F1-score (0.193), whereas logistic regression showed the highest AUROC (0.705) and sensitivity (0.589). SHAP analyses identified medication count, sodium level, diabetes mellitus, and comorbidity burden as major contributors to ADE risk prediction. Conclusions: Explainable ML models demonstrated moderate but clinically meaningful performance for predicting ADEs in older adults with polypharmacy. These findings suggest that explainable ML approaches may support clinically interpretable medication safety risk stratification in real-world clinical practice.