TreatRAG: A Framework for Personalized Treatment Recommendation
C. Liu, Hao-Ren Yao, Der‐Chen Chang, Ophir Frieder · 2025
Medication recommendation is a critical function of clinical decision support systems, directly influencing patient safety and treatment efficacy.While large language models (LLMs) show promise in clinical tasks such as summarization and question answering, their ability to make accurate treatment predictions remains limited, in part, due to their lack of specialized medical knowledge and exposure to real-world patient data.We introduce TreatRAG, an interpretable, model-agnostic retrieval-augmented generation (RAG) framework aimed at early-stage development to enhance medication recommendation accuracy using publicly available clinical data; thus, TreatRAG forms a critical foundational step toward future clinical validation and domain expert involvement.TreatRAG retrieves similar patient cases, i.e., so called "digital twins", using interpretable N-gram Jaccard similarity and augments the input prompt to ground LLM predictions in real clinical scenarios.We evaluate our framework on the MIMIC-IV dataset using BioGPT, BioMistral, Phi3, and Flan-T5.TreatRAG-enhanced BioGPT improves its F1-score from 0.14 to 0.34, BioMistral from 0.22 to 0.54, Phi-3 from 0.09 to 0.16, and Flan-T5 from 0.23 to 0.30, while also lowering, often significantly, the hallucination rate.Our model-agnostic framework offers a flexible, effective, and interpretable solution to advance the reliability of LLMs in clinical decision support.