Artificial intelligence (AI) in healthcare has significantly advanced intelligent medical treatment. However, traditional intelligent healthcare is limited by static data and unified standards, preventing full integration with individual situations and other challenges. Hence, a more professional and detailed intelligent healthcare method is needed for development. To this end, we propose an innovative framework named Heath-LLM, which combines large-scale feature extraction and medical knowledge trade-off scoring. Compared to traditional health management methods, our approach has three main advantages. First, our method integrates health reports into a large model to provide detailed task information. Second, professional medical expertise is used to adjust the weighted scores of health characteristics. Third, we use a semi-automated feature extraction framework to enhance the analytical power of language models and incorporate expert insights to improve the accuracy of disease prediction. We have conducted disease prediction experiments on a large number of health reports to assess the effectiveness of Health-LLM. The results of the experiments indicate that the proposed method surpasses traditional methods and has the potential to revolutionize disease prediction and personalized health management. The code is available at https://github.com/jmyissb/HealthLLM.

翻译：医疗领域中的人工智能已显著推动了智能医疗的发展。然而，传统智能医疗受限于静态数据和统一标准，难以实现与个体情况的充分融合，并面临其他挑战。因此，需要一种更专业、更精细的智能医疗方法以推动发展。为此，我们提出了一种创新框架，命名为Health-LLM，该方法结合了大规模特征提取与医学知识权衡评分。与传统健康管理方法相比，我们的方法具有三大优势：首先，将健康报告整合至大模型中，以提供细化的任务信息；其次，利用专业医学知识调整健康特征的加权评分；第三，采用半自动化特征提取框架增强语言模型的分析能力，并融入专家见解以提高疾病预测的准确性。我们基于大量健康报告进行了疾病预测实验，以评估Health-LLM的效果。实验结果表明，所提出的方法优于传统方法，并有望革新疾病预测与个性化健康管理。代码请见 https://github.com/jmyissb/HealthLLM。