Health monitoring systems have revolutionized modern healthcare by enabling the continuous capture of physiological and behavioral data, essential for preventive measures and early health intervention. While integrating this data with Large Language Models (LLMs) has shown promise in delivering interactive health advice, traditional methods like Retrieval-Augmented Generation (RAG) and fine-tuning often fail to fully utilize the complex, multi-dimensional, and temporally relevant data from wearable devices. These conventional approaches typically provide limited actionable and personalized health insights due to their inadequate capacity to dynamically integrate and interpret diverse health data streams. In response, this paper introduces a graph-augmented LLM framework designed to significantly 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 dynamic feature importance scores derived 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, addressing the critical need for models that process and interpret complex health data effectively. Our findings show that augmenting prompts with our framework yields significant improvements in all 4 criteria. Through our framework, we can elicit well-crafted, more thoughtful responses tailored to a specific patient.

通过引入图增强型LLM框架，本文试图显著提高个性化和清晰度的健康洞察力，利用层次图结构捕捉病人间和病人内部关系，并丰富LLM提示信息，通过一个睡眠分析案例研究在COVID-19封锁期间涉及20名大学生，展示了我们的模型在有效生成可行和个性化健康洞察力方面的潜力，并利用另一个LLM评估了这些洞察力的相关性、完整性、可操作性和个性化程度，从而满足有效处理和解释复杂健康数据的模型的重要需求。我们的研究结果表明，使用我们的框架增强提示信息在所有4个标准下都取得了显著改进，通过我们的框架，我们可以产生量身定制、更慎重的回应，以适应特定的病人。

基于图增强的个性化健康洞察的LLMs：对睡眠分析的案例研究