RAG for Personalized Medicine: A Framework for Integrating Patient Data and Pharmaceutical Knowledge for Treatment Recommendations

Personalized medicine aims to tailor treatment strategies to individual patients by integrating diverse medical data and pharmaceutical knowledge. To address challenges such as data heterogeneity, knowledge retrieval, and safety evaluation, this paper proposes a novel framework utilizing a dynamic Retrieval-Augmented Generation (RAG). The framework integrates multi-modal patient data, including electronic health records (EHRs) and genomic information, with pharmaceutical knowledge bases such as DrugBank and PubMed. A dynamic retrieval mechanism is designed to extract relevant knowledge in real-time, while contextual filtering ensures recommendations are both accurate and clinically safe. Through extensive experiments on simulated patient scenarios, the proposed framework demonstrates significant improvements in recommendation precision, relevance, and safety compared to baseline methods. Results show that the approach provides a reliable and interpretable system for personalized drug recommendations, offering new perspectives for advancing decision support in personalized medicine.