Pengembangan model prediksi drug related problems menggunakan machine learning pada pasien geriatri dengan polifarmasi

Ika Sutra Perwirahayu Aji Saputri (STIKes Bhakti Husada Mulia)

JOURNAL OF Pharmacy and Tropical Issues
Vol. 6 No. 3 (2026): July Edition 2026

Background: Drug Related Problems (DRPs) are a major cause of reduced therapeutic quality, prolonged hospital stays, and high healthcare costs among geriatric patients. Polypharmacy and multimorbidity increase therapeutic complexity, necessitating predictive methods capable of early identification of high-risk patients. Purpose: To develop a machine learning-based prediction model for drug related problems (DRPs) in geriatric patients with polypharmacy. Method: A retrospective, analytical observational study design was employed, utilizing electronic medical record data from hospitalized geriatric patients. A total of 2,458 patients meeting the inclusion criteria were analyzed. Prediction models were developed using Logistic Regression, Decision Tree, Random Forest, Support Vector Machine, and Extreme Gradient Boosting (XGBoost). Model evaluation was conducted using Accuracy, Precision, Recall, F1 score, and the Area Under the Receiver Operating Characteristic Curve (ROC-AUC). Model interpretation was performed using Shapley Additive Explanations (SHAP). Results: A total of 70.6% of patients experienced DRPs, with the most common category being treatment effectiveness. Factors significantly associated with the occurrence of DRPs included advanced age, the number of medications, chronic kidney disease, the use of high-alert medications, and major drug interactions (p<0.05). Conclusion: The incidence of Drug Related Problems (DRPs) among geriatric patients undergoing polypharmacy is high, predominantly within the Treatment Effectiveness category. The XGBoost algorithm proved to be the most effective at predicting DRPs, with key predictors including the number of medications, drug interactions, renal function, age, and High Alert Medications. Suggestion: Future research should conduct external validation using multicenter data from various hospitals. Additionally, the integration of the model into hospital information systems needs to be evaluated through prospective studies to assess its impact on reducing the incidence of DRPs.   Keywords: Clinical Decision Support System; Drug Related Problems; Machine Learning; Older Adults; Polypharmacy; XGBoost.