# Traditional Chinese Medicine Case Analysis System for High-Level Semantic Abstraction: Optimized with Prompt and RAG

arXiv: https://arxiv.org/abs/2411.15491 · HTML: https://mlsanity.com/paper/2411.15491/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 22 references that are in the map.
- First posted on arXiv 23 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [Chain-of-Thought Prompting Elicits Reasoning in Large Language Models](https://mlsanity.com/paper/2201.11903/) (2022)
- [Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks](https://mlsanity.com/paper/2005.11401/) (2020)
- [Qwen2.5-Coder Technical Report](https://mlsanity.com/paper/2409.12186/) (2024)
- [DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model](https://mlsanity.com/paper/2405.04434/) (2024)
- [ERNIE: Enhanced Representation through Knowledge Integration](https://mlsanity.com/paper/1904.09223/) (2019)
- [mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval](https://mlsanity.com/paper/2407.19669/) (2024)
- [Zhongjing: Enhancing the Chinese Medical Capabilities of Large Language Model through Expert Feedback and Real-world Multi-turn Dialogue](https://mlsanity.com/paper/2308.03549/) (2023)
- [HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extraction](https://mlsanity.com/paper/2408.04948/) (2024)
- [LLM4Rerank: LLM-based Auto-Reranking Framework for Recommendations](https://mlsanity.com/paper/2406.12433/) (2024)
- [Toward General Design Principles for Generative AI Applications](https://mlsanity.com/paper/2301.05578/) (2023)
- [TCMBench: A Comprehensive Benchmark for Evaluating Large Language Models in Traditional Chinese Medicine](https://mlsanity.com/paper/2406.01126/) (2024)
- [Qibo: A Large Language Model for Traditional Chinese Medicine](https://mlsanity.com/paper/2403.16056/) (2024)
- [TCM-SD: A Benchmark for Probing Syndrome Differentiation via Natural Language Processing](https://mlsanity.com/paper/2203.10839/) (2022)

## Built on by (most cited first, 1 of 1)

- [Improving TCM Question Answering through Tree-Organized Self-Reflective Retrieval with LLMs](https://mlsanity.com/paper/2502.09156/) (2025)

## Abstract (arXiv)

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 BaiduERNIESpeed128K 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.