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.

本文提出了一种新颖的树形结构推荐系统HiRMed，利用增强检索生成(RAG)技术提供智能医学检测推荐。该系统通过在每个树节点进行医疗推理，显著提高了传统向量相似性方法的效率和准确性，实验结果表明其在覆盖率、准确性和漏检率方面优于传统方法，表明该研究在医学检测推荐领域的重大进展。 

基于树形结构的RAG-Agent推荐系统：医学检测数据的案例研究