Machine learning driven diabetes care using predictive-prescriptive analytics for personalized medication prescription.

ABSTRACT: The increasing prevalence of type 2 diabetes (T2D) is a significant health concern worldwide. Effective and personalized treatment strategies are essential for improving patient outcomes and reducing healthcare costs. Machine learning (ML) has the potential to create clinical decision support systems (CDSS) that assist clinicians in making prediction-informed treatment decisions. This study aims to develop a novel predictive-prescriptive analytics framework that leverages ML to enhance medication prescriptions for T2D patients. The framework is designed as a data-driven CDSS to determine the best medication strategies based on individual patient profiles, including demographics, comorbidities, and medications. Utilizing a comprehensive dataset of electronic health records from 17,773 patie

SUBMITTER: Zargoush M 

PROVIDER: S-EPMC12287355 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature