Knowledge of the medical decision process, which can be modeled as medical
decision trees (MDTs), is critical to build clinical decision support systems.
However, the current MDT construction methods rely heavily on time-consuming
and laborious manual annotation. In this work, we propose a novel task,
Text2MDT, to explore the automatic extraction of MDTs from medical texts such
as medical guidelines and textbooks. We normalize the form of the MDT and
create an annotated Text-to-MDT dataset in Chinese with the participation of
medical experts. We investigate two different methods for the Text2MDT tasks:
(a) an end-to-end framework which only relies on a GPT style large language
models (LLM) instruction tuning to generate all the node information and tree
structures. (b) The pipeline framework which decomposes the Text2MDT task to
three subtasks. Experiments on our Text2MDT dataset demonstrate that: (a) the
end-to-end method basd on LLMs (7B parameters or larger) show promising
results, and successfully outperform the pipeline methods. (b) The
chain-of-thought (COT) prompting method \cite{Wei2022ChainOT} can improve the
performance of the fine-tuned LLMs on the Text2MDT test set. (c) the
lightweight pipelined method based on encoder-based pretrained models can
perform comparably with LLMs with model complexity two magnititudes smaller.
Our Text2MDT dataset is open-sourced at
\url{this https URL}, and the source codes are
open-sourced at \url{this https URL}.

通过自动提取医疗文本中的医疗决策树，我们提出了一种新的任务 Text2MDT，探索构建临床决策支持系统的能力。使用两种方法进行 Text2MDT 任务的研究，一个是只依赖大型语言模型（LLM）指导的端到端框架，另一个是将 Text2MDT 任务分解为三个子任务的流水线框架。实验结果表明，使用 LLMs 的端到端方法在 Text2MDT 数据集上表现出色，优于流水线方法。