Pengembangan model prediksi drug related problems menggunakan machine learning pada pasien geriatri dengan polifarmasi
DOI: https://doi.org/10.56922/pti.v6i3.3826
Clinical Decision Support System Drug Related Problems Machine Learning Older Adults Polypharmacy XGBoost
Abstract
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.
Downloads
References
Aini, F. D., Siswoyo, I. A., Fahruddin, A., & Prasetyo, H. (2026). Artificial intelligence: Konsep lanjut dan di era digital. PT Bukuloka Literasi Bangsa.
Andayani, T. M., Rahmawati, F., Rokhman, M. R., Mayasari, G., Nurcahya, B. M., Arini, Y. D., & Intiyani, R. (2020). Drug related problems: identifikasi faktor risiko dan pencegahannya. UGM PRESS.
Astuti, S. Y., Ihsan, M., & Rahmawati, F. (2020). Hubungan antara drug-related problems dan lama rawat inap pada pasien dengan diabetes tipe 2. Diabetes, 4, 5.
Awali, J. D., Pardilawati, C. Y., Soleha, T. U., & Oktarlina, R. Z. (2024). Kajian polifarmasi terhadap keamanan obat pada pasien geriatri. Medical Profession Journal of Lampung, 14(4), 739-745.
Hasballah, K. (2022). Farmakologi geriatri. Syiah Kuala University Press.
Irmawati, I., & Aziz, F. (2025). Adverse Drug Reactions (ADR) Prediction using random forest algorithm and neural networks. Integrated Journal of Pharmacy Innovations, 1(1), 6-9.
Islami, A. (2025). Permasalahan terkait penggunaan obat (drug related problem) pada pasien lansia di negara-negara berkembang. Sibatik Journal: Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan, 5(1), 443-452.
Kotijah, S., & Priastana, I. K. A. (2020). Efektivitas cognitive behaviour therapy dalam mengurangi gejala post traumatic stress disorder: Systematic review. Jurnal Kesehatan Terpadu (Integrated Health Journal), 11(1), 41-51.
Kushariyadi, K., Apriyanto, H., Herdiana, Y., Asy’ari, F. H., Judijanto, L., Pasrun, Y. P., & Mardikawati, B. (2024). Artificial intelligence: Dinamika perkembangan AI beserta penerapannya. PT. Sonpedia Publishing Indonesia.
Masnoon, N., Shakib, S., Kalisch-Ellett, L., & Caughey, G. E. (2017). What is polypharmacy? A systematic review of definitions. BMC Geriatrics, 17(1), 230.
Nabila, H. A., & Pamungkas, E. W. (2025). Perbandingan algoritma machine learning: svm, random forest, dan xgboost untuk prediksi stroke: comparison of machine learning algorithms: svm, random forest, and xgboost for stroke prediction. Rabit: Jurnal Teknologi dan Sistem Informasi Univrab, 10(2), 1098-1110.
Nurfauzi, Y., Wahyono, D., Rahmawati, F., & Yasin, N. M. (2020). Inovasi home care apoteker melalui supervisi penggunaan obat geriatri untuk meningkatkan kepatuhan terapi penyakit kronis. Indonesia J Clin Pharm, 9(2), 147.
O’Mahony, D., Cherubini, A., Guiteras, A. R., Denkinger, M., Beuscart, J. B., Onder, G., & Curtin, D. (2023). STOPP/START criteria for potentially inappropriate prescribing in older people: version 3. European Geriatric Medicine, 14(4), 625-632.
Pharmaceutical Care Network Europe. (2020). The PCNE classification for drug-related problems V9.1. Diakses dari: https://pcne.org/wp-content/uploads/2026/02/PCNE_Working-Groups-Classification_V9-1_final.pdf
Primadhini, T. A., Pratama, D., Deniyati, A., & Rahmawati, A. (2026). Farmasi klinis: Konsep, terapi, dan praktik pelayanan kefarmasian. PT Cipta Digital Edukasi.
Rankin, A., Cadogan, C., Cooper, J., Patterson, S. M., Kerse, N., Cardwell, C. R., & Hughes, C. (2018). Interventions to improve the appropriate use of polypharmacy for older people: an updated Cochrane systematic review. International Journal of Pharmacy Practice, 26(S1), 5-6.
Siregar, S. R., Fitriany, J., Utariningsih, W., Nasution, M. H. F., Balqis, K. M., & Miranda, M. (2025). Analisis tingkat polifarmasi dan kepatuhan pengobatan terhadap risiko drug related problems pada pasien prolanis di Puskesmas Dewantara Aceh Utara. GALENICAL: Jurnal Kedokteran dan Kesehatan Mahasiswa Malikussaleh, 4(6), 13-23.
Utaminingsih, E., Oktasari, S. R., & Cholisoh, Z. (2025). Drug related problems (DRPs) pada pasien mata rawat inap: Kajian potong lintang retrospektif selama dua tahun. Journal of Pharmaceutical and Sciences, 2311-2320.
World Health Organization. (2024). WHO clinical consortium on healthy ageing 2023: Meeting report. World Health Organization. Diakses dari: https://www.who.int/publications/i/item/9789240093546
Zahniar, Z., Khairunnisa, K., & Wiryanto, W. (2025). Analysis of drug-related problems in prescriptions of type 2 diabetes mellitus patients at Hospital “X”, Aceh Province: A retrospective cross-sectional study. Journal of Pharmaceutical and Sciences, 2689-2698.
Downloads
Published
Issue
Section
License
Copyright (c) 2026 JOURNAL OF Pharmacy and Tropical Issues
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
