摘要： 线上“轻问诊”决策树是为轻症患者提供导诊科室、初步诊断或治疗建议的问诊决策树。基于医学文献文本构建“轻问诊”决策树，无法满足真实“轻问诊”场景中情况各异患者的问诊需求，在研究特定疾病领域最新进展方面也存在滞后性。若由医学专家基于个体经验人工构建“轻问诊”决策树，不仅效率低，而且缺乏统一的标准化表征。因此提出了一个全新的决策树生成任务，基于线上“轻问诊”对话文本数据集生成决策树（Medical Decision Tree Generation based on Online Light Consultation (DTGOLC)。面向该任务，提出了基于大语言模型的“轻问诊”决策信息摘要生成方法LCDTSG-LLM (Light Consultation Decision Text Summarization Generation Method Based on Large Language Models)和基于问诊决策路径融合的“轻问诊”决策树生成方法MDPFDT（Medical Decision Path Fusion for Decision Tree）。本文生成了5547条决策路径以及近30棵“轻问诊”决策树。最终本文将决策树集合作为外部知识库进行了检索增强生成（RAG）实验，实验结果表明，本文生成的决策树在辅助轻问诊决策任务方面的表现显著优于基线模型，其F1分数相较于以原始问诊对话文本作为知识库的基线模型，平均提高达27.58%。

Abstract: Online "Light Consultation" decision trees are designed to provide patients with minor health issues guidance on appropriate departments, preliminary diagnoses, or treatment suggestions. However, constructing such decision trees based solely on medical literature fails to meet the diverse needs of patients in real-world light consultation scenarios and suffers from delays in reflecting the latest advances in specific disease areas. While medical experts can manually construct these trees, the process is inefficient and lacks standardized representation due to reliance on individual experience. To address these limitations, this paper proposes a novel task: generating decision trees from online light consultation dialogue texts (DTGOLC). For this task, we introduce two methods: a large language model-based approach for light consultation decision text summarization generation (LCDTSG-LLM), and a medical decision path fusion method for decision tree generation (MDPFDT). Our study constructs 5,547 decision paths and generates nearly 30 light consultation decision trees. Finally, these decision trees are subsequently integrated as an external knowledge base in a retrieval-augmented generation (RAG) framework. Experimental results demonstrate that the proposed decision trees significantly outperform baseline models in assisting lightweight diagnostic decision-making tasks, achieving an average improvement in F1-score of 27.58% compared to the baseline using original consultation dialogues as the knowledge base.