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.

提出了可用于大型语言模型的 LLM 执行的临床指导树（CGT），并根据临床实践指南中的流程图构建了医学诊断决策数据集（MedDM），从医学文献中筛选出流程图并将其转化为标准化的诊断决策树，构建了包含 1202 个决策树的知识库，涵盖了内科、外科、精神科等 12 个医院科室和 500 多种疾病，同时提出了对 LLM 可执行 CGT 的推理方法和患者 - LLM 多轮对话框架。