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.

翻译：人工智能（尤其是大语言模型）的最新进展显著推动了医疗健康应用的发展，并在智能诊疗方面展现出巨大潜力。然而，现有医疗AI系统仍面临数据体量庞大、症状表征标准不一致等突出挑战，难以充分满足个体患者的实际需求。为推进专业化与个性化的医疗健康服务，本文提出一种创新框架Health-LLM，该框架融合了大规模特征提取与医疗知识权衡评分机制。与传统健康管理应用相比，本系统具备三大优势：（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）采用检索增强生成机制以强化特征提取能力；（3）引入半自动化特征更新框架，支持特征融合与删除操作以提升疾病预测准确率。我们在大量健康报告数据集上对Health-LLM系统进行了实验评估，结果表明所提系统性能优于现有方法，在疾病预测与个性化健康管理领域具有重要推进作用。