Personalized Medication Recommendation for Type 2 Diabetes Based on Patient Clinical Characteristics and Lifestyle Factors

Abstract

Type 2 diabetes mellitus represents a significant global health challenge requiring personalized therapeutic approaches to optimize patient outcomes. This study presents a comprehensive framework for personalized medication recommendation that integrates patient clinical characteristics with lifestyle factors to enhance treatment efficacy. The proposed methodology employs multi-dimensional patient profiling combined with clinical guideline integration to develop a robust recommendation algorithm. Clinical validation demonstrates superior performance compared to traditional approaches, with recommendation accuracy reaching 89.3% and clinical concordance of 92.1%. The framework successfully addresses individual patient variability through sophisticated feature engineering and patient subgroup analysis. Performance evaluation across diverse patient cohorts reveals significant improvements in glycemic control prediction and medication adherence rates. Expert clinical evaluation confirms the practical applicability of the system in real-world healthcare environments. The study contributes novel insights into personalized diabetes management through evidence-based computational approaches that bridge clinical expertise with patient-specific characteristics.