TCMLCM: an intelligent question-answering model for traditional Chinese medicine lung cancer based on the KG2TRAG method
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) met...
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| Format: | Article |
| Language: | English |
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KeAi Communications Co., Ltd.
2025-03-01
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| Series: | Digital Chinese Medicine |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589377725000291 |
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| author | Zhou Chunfang Gong Qingyue Zhan Wendong Zhu Jinyang Luan Huidan |
| author_facet | Zhou Chunfang Gong Qingyue Zhan Wendong Zhu Jinyang Luan Huidan |
| author_sort | Zhou Chunfang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-ddd9662a244a4247b8085bb6f52e2817 |
| institution | Kabale University |
| issn | 2589-3777 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Digital Chinese Medicine |
| spelling | doaj-art-ddd9662a244a4247b8085bb6f52e28172025-08-20T03:49:45ZengKeAi Communications Co., Ltd.Digital Chinese Medicine2589-37772025-03-0181364510.1016/j.dcmed.2025.03.011TCMLCM: an intelligent question-answering model for traditional Chinese medicine lung cancer based on the KG2TRAG methodZhou Chunfang0Gong Qingyue1Zhan Wendong2Zhu Jinyang3Luan Huidan4School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, ChinaSchool of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China; Jiangsu Province Engineering Research Center of TCM Intelligence Health Service, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, China; Corresponding author:School of Life Science, Beijing Institute of Technology, Beijing 100081, ChinaSchool of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, ChinaSchool of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu 210023, ChinaObjective: 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.http://www.sciencedirect.com/science/article/pii/S2589377725000291Traditional Chinese medicine (TCM)Lung cancerQuestion-answeringLarge language modelFine-tuningKnowledge graph |
| spellingShingle | Zhou Chunfang Gong Qingyue Zhan Wendong Zhu Jinyang Luan Huidan TCMLCM: an intelligent question-answering model for traditional Chinese medicine lung cancer based on the KG2TRAG method Digital Chinese Medicine Traditional Chinese medicine (TCM) Lung cancer Question-answering Large language model Fine-tuning Knowledge graph |
| title | TCMLCM: an intelligent question-answering model for traditional Chinese medicine lung cancer based on the KG2TRAG method |
| title_full | TCMLCM: an intelligent question-answering model for traditional Chinese medicine lung cancer based on the KG2TRAG method |
| title_fullStr | TCMLCM: an intelligent question-answering model for traditional Chinese medicine lung cancer based on the KG2TRAG method |
| title_full_unstemmed | TCMLCM: an intelligent question-answering model for traditional Chinese medicine lung cancer based on the KG2TRAG method |
| title_short | TCMLCM: an intelligent question-answering model for traditional Chinese medicine lung cancer based on the KG2TRAG method |
| title_sort | tcmlcm an intelligent question answering model for traditional chinese medicine lung cancer based on the kg2trag method |
| topic | Traditional Chinese medicine (TCM) Lung cancer Question-answering Large language model Fine-tuning Knowledge graph |
| url | http://www.sciencedirect.com/science/article/pii/S2589377725000291 |
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