It is becoming increasingly emphasis on the importance of LLM participating in clinical diagnosis decision-making. However, the low specialization refers to that current medical LLMs can not provide specific medical advice, which are more like a medical Q\&A. And there is no suitable clinical guidance tree data set that can be used directly with LLM. To address this issue, we first propose LLM-executavle clinical guidance tree(CGT), which can be directly used by large language models, and construct medical diagnostic decision-making dataset (MedDM), from flowcharts in clinical practice guidelines. We propose an approach to screen flowcharts from medical literature, followed by their identification and conversion into standardized diagnostic decision trees. Constructed a knowledge base with 1202 decision trees, which came from 5000 medical literature and covered 12 hospital departments, including internal medicine, surgery, psychiatry, and over 500 diseases.Moreover, we propose a method for reasoning on LLM-executable CGT and a Patient-LLM multi-turn dialogue framework.

翻译：随着大语言模型参与临床诊断决策的重要性日益凸显，当前医疗大语言模型因专业化程度不足，实际表现更接近医疗问答系统，难以提供具体医疗建议，且缺乏可与大语言模型直接适配的临床指导树数据集。针对此问题，我们首先提出LLM可执行临床指导树（CGT），该结构可直接被大语言模型调用，并基于临床实践指南中的流程图构建了医疗诊断决策数据集（MedDM）。我们提出了从医学文献中筛选流程图的方法，进而识别并将其转化为标准化诊断决策树。由此构建的知识库包含来自5000篇医学文献的1202棵决策树，覆盖内科、外科、精神科等12个医院科室的500余种疾病。此外，我们提出了一种面向LLM可执行CGT的推理方法及患者-大语言模型多轮对话框架。