构建中医临床案例库并优化检索，提升诊疗推理准确性
Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG
- 用爬虫收集5000+中医案例，结构化关键诊疗信息
- 结合RAG与双阶段检索，准确率显著提升
- 适合中医AI研究者与临床辅助系统开发者
本文提出一种构建中医临床案例数据库的技术方案，通过网络爬虫从360doc等多平台采集超过5000例中医临床病例，完成数据清洗并结构化患者信息、病机、证候及标注等关键字段。利用百度ERNIE Speed 128K API去除冗余内容，通过DeepSeek-v2 API生成标准JSON格式的最终答案。在检索阶段采用RAG与重排序技术优化召回，并设计混合匹配策略，结合两阶段检索与基于Jieba的关键词匹配，显著提升了模型输出的准确性。
原文摘要 · Abstract (English)
This paper details a technical plan for building a clinical case database for Traditional Chinese Medicine (TCM) using web scraping. Leveraging multiple platforms, including 360doc, we gathered over 5,000 TCM clinical cases, performed data cleaning, and structured the dataset with crucial fields such as patient details, pathogenesis, syndromes, and annotations. Using the $Baidu\_ERNIE\_Speed\_128K$ API, we removed redundant information and generated the final answers through the $DeepSeekv2$ API, outputting results in standard JSON format. We optimized data recall with RAG and rerank techniques during retrieval and developed a hybrid matching scheme. By combining two-stage retrieval method with keyword matching via Jieba, we significantly enhanced the accuracy of model outputs.
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