  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.
