TCMLCM:an intelligent question-answering model for traditional Chinese medicine lung cancer based on the KG2TRAG method  

TCMLCM:基于KG2TRAG方法的中医肺癌智能问答模型

作　　者：Chunfang ZHOU Qingyue GONG Wendong ZHAN Jinyang ZHU Huidan LUAN 周春芳;龚庆悦;詹文栋;朱金阳;栾慧丹(南京中医药大学人工智能与信息技术学院,江苏南京210023;南京中医药大学江苏省智慧中医药健康服务工程研究中心,江苏南京210023;北京理工大学生命学院,北京100081)

机构地区：[1]School of Artificial Intelligence and Information Technology,Nanjing University of Chinese Medicine,Nanjing,Jiangsu 210023,China [2]Jiangsu Province Engineering Research Center of TCM Intelligence Health Service,Nanjing University of Chinese Medicine,Nanjing,Jiangsu 210023,China [3]School of Life Science,Beijing Institute of Technology,Beijing 100081,China

出　　处：《Digital Chinese Medicine》2025年第1期36-45,共10页数字中医药(英文)

基　　金：Postgraduate Research&Practice Innovation Program of Jiangsu Province(KYCX24_2145).

摘　　要：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 retrievalaugmented generation(KG2TRAG)method.Methods The TCM lung cancer model(TCMLCM)was constructed by fine-tuning Chat-GLM2-6B 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.目的利用从知识图谱到文本增强的检索增强生成(KG2TRAG)的方法将大型语言模型与结构化知识图相结合,提高中医肺癌问答模型的准确性和专业性。方法通过在Tianchi TCM、HuangDi和Shen-Nong-TCM-Dataset数据集以及中医肺癌知识图谱上对ChatGLM2-6B进行微调,构建了中医肺癌模型(TCMLCM)。为增强知识检索能力,引入KG2TRAG方法借助ChatGPT辅助线性化将知识图谱三元组转换为自然语言文本,并利用大型语言模型进行上下文感知推理。为了进行全面比较,选择MedicalGPT、HuatuoGPT和BenTsao作为基线模型。使用双语评估替代(BLEU)、面向召回的自动摘要评估(ROUGE)、准确率以及领域特定的TCM-LCEval指标对性能进行评估,并由中医肿瘤学专家对答案的准确性、专业性和可用性进行验证。结果TCMLCM模型在所有指标中均取得最优性能,其中BLEU得分为32.15%,ROUGE-L为59.08%,准确率为79.68%。值得注意的是,在针对中医领域的TCM-LCEval评估中,其性能比基线模型高3%~12%。专家评估显示,在准确性和专业性方面表现卓越。结论TCMLCM为中医肺癌问答提供了一种创新的解决方案,证明了将结构化的知识图谱与大型语言模型相结合的可行性。这项工作推动了中医智能医疗工具的发展,并为传统医学中未来的AI驱动应用奠定了基础。

关 键 词：Traditional Chinese medicine(TCM) Lung cancer Question-answering Large language model Fine-tuning Knowledge graph KG2TRAG method 

分 类 号：R273[医药卫生—中西医结合]