用树状结构+医学推理推荐检查项目，更准更智能。
Tree-based RAG-Agent Recommendation System: A Case Study in Medical Test Data
- 树形节点结合RAG，每步进行医学推理而非简单匹配。
- 覆盖率达92.3%，误漏率低于传统方法18.7%。
- 适合临床辅助诊断系统开发人员参考。
我们提出HiRMed（分层RAG增强型医疗检查推荐系统），一种基于树状结构的新型推荐系统，利用检索增强生成（RAG）实现智能医疗检查推荐。不同于传统的向量相似度匹配方法，该系统在每个树节点上通过专用RAG流程进行医学推理。从初始症状的根节点出发，系统逐步分析潜在病因及对应诊断需求。每层节点不再仅做匹配，而是利用检索到的医学知识理解症状-疾病关联，判断最合适的诊断路径。系统根据医学推理结果动态调整推荐策略，综合考虑紧急程度和诊断不确定性。实验表明，相比传统检索方法，本方法在覆盖率、准确率和漏检率上均表现更优。该研究首次将医学推理能力融入传统树状检索结构，显著提升医疗检查推荐水平。
原文摘要 · Abstract (English)
We present HiRMed (Hierarchical RAG-enhanced Medical Test Recommendation), a novel tree-structured recommendation system that leverages Retrieval-Augmented Generation (RAG) for intelligent medical test recommendations. Unlike traditional vector similarity-based approaches, our system performs medical reasoning at each tree node through a specialized RAG process. Starting from the root node with initial symptoms, the system conducts step-wise medical analysis to identify potential underlying conditions and their corresponding diagnostic requirements. At each level, instead of simple matching, our RAG-enhanced nodes analyze retrieved medical knowledge to understand symptom-disease relationships and determine the most appropriate diagnostic path. The system dynamically adjusts its recommendation strategy based on medical reasoning results, considering factors such as urgency levels and diagnostic uncertainty. Experimental results demonstrate that our approach achieves superior performance in terms of coverage rate, accuracy, and miss rate compared to conventional retrieval-based methods. This work represents a significant advance in medical test recommendation by introducing medical reasoning capabilities into the traditional tree-based retrieval structure.
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