Recent advancements in artificial intelligence (AI), especially large language models (LLMs), have significantly advanced healthcare applications and demonstrated potentials in intelligent medical treatment. However, there are conspicuous challenges such as vast data volumes and inconsistent symptom characterization standards, preventing full integration of healthcare AI systems with individual patients' needs. To promote professional and personalized healthcare, we propose an innovative framework, Heath-LLM, which combines large-scale feature extraction and medical knowledge trade-off scoring. Compared to traditional health management applications, our system has three main advantages: (1) It integrates health reports and medical knowledge into a large model to ask relevant questions to large language model for disease prediction; (2) It leverages a retrieval augmented generation (RAG) mechanism to enhance feature extraction; (3) It incorporates a semi-automated feature updating framework that can merge and delete features to improve accuracy of disease prediction. We experiment on a large number of health reports to assess the effectiveness of Health-LLM system. The results indicate that the proposed system surpasses the existing ones and has the potential to significantly advance disease prediction and personalized health management. The code is available at https://github.com/jmyissb/HealthLLM.

翻译：近年来，人工智能（AI）尤其是大型语言模型（LLM）的进展显著推动了医疗应用，并在智能诊疗领域展现出潜力。然而，海量数据规模与症状特征表征标准不统一等挑战依然突出，阻碍了医疗AI系统与个体患者需求的全面融合。为促进专业化和个性化医疗服务，我们提出了一种创新框架——Health-LLM，融合了大规模特征提取与医学知识权衡评分机制。与传统健康管理应用相比，该系统具有三大优势：（1）将健康报告与医学知识整合至大模型中，通过向大语言模型提问实现疾病预测；（2）利用检索增强生成（RAG）机制强化特征提取；（3）引入半自动化特征更新框架，可对特征进行合并与删除，从而提升疾病预测准确性。我们基于大量健康报告开展了实验，评估Health-LLM系统的有效性。结果表明，所提系统性能优于现有方案，有望显著推动疾病预测与个性化健康管理的发展。相关代码已开源：https://github.com/jmyissb/HealthLLM。