用于个性化健康洞察的图增强LLM：以睡眠分析为例的案例研究
Graph-Augmented LLMs for Personalized Health Insights: A Case Study in Sleep Analysis
AI总结：
本文提出图增强LLM框架，利用分层图结构和随机森林动态特征重要性分数丰富提示，在睡眠分析案例中显著提升了健康洞察的个性化、相关性、全面性和可操作性。
摘要：
健康监测系统通过实现对生理和行为数据的连续捕获，彻底改变了现代医疗保健，这对于预防措施和早期健康干预至关重要。虽然将此数据与大型语言模型（LLMs）集成在提供交互式健康建议方面显示出前景，但传统方法如检索增强生成（RAG）和微调通常未能充分利用来自可穿戴设备的复杂、多维和时间相关的数据。这些传统方法通常提供有限的可操作和个性化健康洞察，因为它们在动态集成和解释多样化健康数据流方面能力不足。作为回应，本文引入了一个图增强LLM框架，旨在显著增强健康洞察的个性化和清晰度。利用分层图结构，该框架捕获患者间和患者内的关系，通过从随机森林模型导出的动态特征重要性分数丰富LLM提示。通过一项涉及20名大学生在COVID-19封锁期间的睡眠分析案例研究，证明了该方法的有效性，突显了我们的模型高效生成可操作和个性化健康洞察的潜力。我们利用另一个LLM评估洞察的相关性、全面性、可操作性和个性化，解决了有效处理和解释复杂健康数据的模型的关键需求。我们的发现表明，用我们的框架增强提示在所有4个标准上产生显著改进。通过我们的框架，我们可以引发精心制作的、更深思熟虑的、为特定患者量身定制的响应。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
