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：人工智能在医疗领域显著推进了智能医疗的发展。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全面融合，并面临其他挑战。因此，需要一种更专业、更细致的智能医疗方法来推动发展。为此，我们提出了一种名为Health-LLM的创新框架，该框架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，该方法将健康报告整合到大模型中，以提供详细的任务信息。其次，利用专业医学知识调整健康特征的加权得分。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。我们在大量健康报告上进行了疾病预测实验，以评估Health-LLM的有效性。实验结果表明，所提方法优于传统方法，具有革新疾病预测与个性化健康管理的潜力。代码已开源在https://github.com/jmyissb/HealthLLM。