aceDevelopment of a machine learning-based risk warning model for potentially inappropriate medication use among older patients in a hematology service
IntroductionOlder hematology patients often have multiple comorbidities and receive complex medication therapies; this can increase the risk of potentially inappropriate medication (PIM) use. A machine learning model could potentially serve as an automated screening and warning tool to facilitate the identification of PIM and support timely medication review in these patients. The aim of this study is to develop machine learning-based model that provide warnings regarding PIMs use in elderly patients hospitalize…
A logistic regression model trained on 190 older hematology inpatients identified potentially inappropriate medication use with 0.91 AUC, suggesting a usable early-warning screen for pharmacists and physicians.
The work is a retrospective pilot study of 190 patients from one university hospital in Türkiye, so it does not show whether the model works prospectively or improves clinical outcomes.
Evidence
- Peer-reviewedJournal of Oncology Pharmacy Practice2026-10-19
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Truvace Impact Record TRV-2026-2056, v1: “Development of a machine learning-based risk warning model for potentially inappropriate medication use among older patients in a hematology service.” Truvace, 2026-10-21. /record/TRV-2026-2056 (accessed at citation time). sha256 2b194af7669f2b57…
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