Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis

Health monitoring systems have revolutionized mod-ern healthcare by enabling the continuous capture of physio-logical and behavioral data, essential for preventive measures and early intervention. Integrating this data with Large Lan-guage Models (LLMs) shows promise in delivering interactive health advice, but traditional methods like Retrieval-Augmented Generation (RAG) and fine-tuning often fail to fully utilize the complex, multi-dimensional data from wearable devices. These approaches typically provide limited actionable and personalized health insights due to their inadequate capacity to dynamically integrate and interpret diverse health data streams. This pa-per introduces a graph-augmented LLM framework designed to enhance the personalization and clarity of health insights. Utilizing a hierarchical graph structure, the framework captures inter and intra-patient relationships, enriching LLM prompts with feature importance scores from a Random Forest Model. The effectiveness of this approach is demonstrated through a sleep analysis case study involving 20 college students during the COVID-19 lockdown, highlighting the potential of our model to generate actionable and personalized health insights efficiently. We leverage another LLM to evaluate the insights for relevance, comprehensiveness, actionability, and personalization. Our findings show that augmenting prompts with our framework yields significant improvements in all four criteria, eliciting well-crafted, thoughtful responses tailored to a specific patient.