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
this https URL

我们提出了一个名为 Heath-LLM 的创新框架，它结合了大规模特征提取和医学知识权衡评分，与传统的健康管理方法相比，具有三个主要优势，它将健康报告整合到一个大模型中以提供详细的任务信息，使用专业的医学专业知识来调整健康特征的加权得分，并使用半自动特征提取框架来增强语言模型的分析能力，并结合专家见解来提高疾病预测的准确性。我们对大量的健康报告进行了疾病预测实验，结果表明所提出的方法超过了传统方法，并有潜力革命性地改变疾病预测和个性化健康管理。