TCMLCM: an intelligent question-answering model for traditional Chinese medicine lung cancer based on the KG2TRAG method
10.1016/j.dcmed.2025.03.011
- VernacularTitle:TCMLCM：基于 KG2TRAG 方法的中医肺癌智能问答模型
- Author:
Chunfang ZHOU
;
Qingyue GONG
;
Wendong ZHAN
;
Jinyang ZHU
;
Huidan LUAN
- Publication Type:Journal Article
- Keywords:
Traditional Chinese medicine (TCM);
Lung cancer;
Question-answering;
Large language model;
Fine-tuning;
Knowledge graph;
KG2TRAG method
- From:
Digital Chinese Medicine
2025;8(1):36-45
- CountryChina
- Language:English
-
Abstract:
[Objective] :To improve the accuracy and professionalism of question-answering (QA) model in traditional Chinese medicine (TCM) lung cancer by integrating large language models with structured knowledge graphs using the knowledge graph (KG) to text-enhanced retrieval-augmented generation (KG2TRAG) method.
[Methods] :The TCM lung cancer model (TCMLCM) was constructed by fine-tuning ChatGLM2-6B on the specialized datasets Tianchi TCM, HuangDi, and ShenNong-TCM-Dataset, as well as a TCM lung cancer KG. The KG2TRAG method was applied to enhance the knowledge retrieval, which can convert KG triples into natural language text via ChatGPT-aided linearization, leveraging large language models (LLMs) for context-aware reasoning. For a comprehensive comparison, MedicalGPT, HuatuoGPT, and BenTsao were selected as the baseline models. Performance was evaluated using bilingual evaluation understudy (BLEU), recall-oriented understudy for gisting evaluation (ROUGE), accuracy, and the domain-specific TCM-LCEval metrics, with validation from TCM oncology experts assessing answer accuracy, professionalism, and usability.
[Results] :The TCMLCM model achieved the optimal performance across all metrics, including a BLEU score of 32.15%, ROUGE-L of 59.08%, and an accuracy rate of 79.68%. Notably, in the TCM-LCEval assessment specific to the field of TCM, its performance was 3% − 12% higher than that of the baseline model. Expert evaluations highlighted superior performance in accuracy and professionalism.
[Conclusion] :TCMLCM can provide an innovative solution for TCM lung cancer QA, demonstrating the feasibility of integrating structured KGs with LLMs. This work advances intelligent TCM healthcare tools and lays a foundation for future AI-driven applications in traditional medicine.
- Full text:2025051621565999123zhouchunfang.pdf
