Volume 39 Issue 10
Oct.  2023
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Article Navigation >  Journal of Nanjing University of traditional Chinese Medicine  > 2023  >  39(10): 967-971
YANG Tao, WANG Xin-yu, ZHU Yao, HU Kong-fa, ZHU Xue-fang. Research Ideas and Methods of Intelligent Diagnosis and Treatment of Traditional Chinese Medicine Driven by Large Language Model[J]. Journal of Nanjing University of traditional Chinese Medicine, 2023, 39(10): 967-971. doi: 10.14148/j.issn.1672-0482.2023.0967
Citation: YANG Tao, WANG Xin-yu, ZHU Yao, HU Kong-fa, ZHU Xue-fang. Research Ideas and Methods of Intelligent Diagnosis and Treatment of Traditional Chinese Medicine Driven by Large Language Model[J]. Journal of Nanjing University of traditional Chinese Medicine, 2023, 39(10): 967-971. doi: 10.14148/j.issn.1672-0482.2023.0967
shu

Research Ideas and Methods of Intelligent Diagnosis and Treatment of Traditional Chinese Medicine Driven by Large Language Model

doi: 10.14148/j.issn.1672-0482.2023.0967
  • 1.

    School of Information Management, Nanjing University, Nanjing 210023, China

  • 2.

    School of Artificial Intelligence and Information Technology, Nanjing University of Chinese Medicine, Nanjing 210023, China

  • 3.

    The First School of Clinical Medicine, Nanjing University of Chinese Medicine, Nanjing 210023, China

  • 4.

    Jiangsu Collaborative Innovation Center of Traditional Chinese Medicine in Prevention and Treatment of Tumor, Nanjing 210023, China

  • 5.

    Jiangsu Provincial Research Center for the Development and Application of Traditional Chinese Medicine External Medicines, Nanjing 210023, China

  • 6.

    Jiangsu Research Center for Major Health Risk Management and TCM Control Policy, Nanjing 210023, China

  • Received Date: 2023-08-02
    Available Online: 2023-11-10
    Abstract
  • Intelligent diagnosis and treatment of traditional Chinese medicine is an important direction for the modern development of traditional Chinese medicine. Large language model technology has promoted the development of artificial intelligence. Combining it with traditional Chinese medicine to build intelligent diagnosis and treatment methods and applications of traditional Chinese medicine driven by large language models is of great significance to the innovative development of traditional Chinese medicine. On the basis of analyzing and summarizing the challenges faced by intelligent diagnosis and treatment of traditional Chinese medicine, it is proposed to build research ideas and methods for intelligent diagnosis and treatment of traditional Chinese medicine driven by large language models based on large language models, covering corpus preparation, knowledge representation, instruction fine-tuning and reinforcement learning, in order to provide reference for intelligent research on traditional Chinese medicine diagnosis and treatment.

     

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    • References(19)
  • [1]
    朱文锋. 证素辨证学[M]. 北京: 人民卫生出版社, 2008: 1-2.

    ZHU WF. Syndrome Differentiation[M]. Beijing: People's medical publishing house, 2008: 1-2.
    [2]
    杨涛, 朱学芳. 中医辨证智能化研究现状及发展趋势[J]. 南京中医药大学学报, 2021, 37(4): 597-601. doi: 10.14148/j.issn.1672-0482.2021.0597

    YANG T, ZHU XF. Discussion on the status and development trend of research on intellectualization of Chinese medicine syndrome differentiation[J]. J Nanjing Univ Tradit Chin Med, 2021, 37(4): 597-601. doi: 10.14148/j.issn.1672-0482.2021.0597
    [3]
    文志华, 夏帅帅, 刘东波, 等. 中医智能辨证诊断技术的演进与问题探讨[J]. 世界科学技术-中医药现代化, 2021, 23(11): 4298-4304. https://www.cnki.com.cn/Article/CJFDTOTAL-SJKX202111050.htm

    WEN ZH, XIA SS, LIU DB, et al. Discussion on the evolution and problems of intelligent syndrome differentiation diagnosis technology in traditional Chinese medicine[J]. Mod Tradit Chin Med Mater Med World Sci Technol, 2021, 23(11): 4298-4304. https://www.cnki.com.cn/Article/CJFDTOTAL-SJKX202111050.htm
    [4]
    韦昌法, 晏峻峰. 从知识表示与推理方法探讨中医数字辨证发展[J]. 中华中医药杂志, 2019, 34(10): 4471-4473. https://www.cnki.com.cn/Article/CJFDTOTAL-BXYY201910004.htm

    WEI CF, YAN JF. Study on Chinese medicine digital syndrome differentiation with its knowledge representation and reasoning methods[J]. China J Tradit Chin Med Pharm, 2019, 34(10): 4471-4473. https://www.cnki.com.cn/Article/CJFDTOTAL-BXYY201910004.htm
    [5]
    刘广, 孙艳秋, 裴媛. 基于C4.5决策树算法的中医胃炎实验数据分类挖掘研究[J]. 中华中医药学刊, 2016, 34(12): 2958-2961. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYHS201612041.htm

    LIU G, SUN YQ, PEI Y. Classified mining of TCM gastritis based on C4.5 decision tree algorithm[J]. Chin Arch Tradit Chin Med, 2016, 34(12): 2958-2961. https://www.cnki.com.cn/Article/CJFDTOTAL-ZYHS201612041.htm
    [6]
    徐璡, 许朝霞, 许文杰, 等. 基于贝叶斯网络原理的835例冠心病病例中医证候分类研究[J]. 上海中医药杂志, 2014, 48(1): 10-13. https://www.cnki.com.cn/Article/CJFDTOTAL-SHZZ201401005.htm

    XU J, XU ZX, XU WJ, et al. Classification of TCM syndromes in 835 cases of coronary heart disease: On the basis of Bayesian networks principle[J]. Shanghai J Tradit Chin Med, 2014, 48(1): 10-13. https://www.cnki.com.cn/Article/CJFDTOTAL-SHZZ201401005.htm
    [7]
    陆萍, 林坤辉, 周昌乐. 基于神经网络的中医面诊证素辨证的研究[J]. 计算机应用研究, 2008, 25(9): 2655-2657. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ200809023.htm

    LU P, LIN KH, ZHOU CL. Research of TCM face diagnosis and symptom factor based on NN[J]. Appl Res Comput, 2008, 25(9): 2655-2657. https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ200809023.htm
    [8]
    DEVLIN J, CHANG MW, LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[EB/OL]. [2023-08-01]. https://arxiv.org/abs/1810.04805.
    [9]
    ZHANG ZY, HAN X, LIU ZY, et al. ERNIE: Enhanced language representation with informative entities[C]//Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics. Florence, Italy. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019: 1441-1451.
    [10]
    TOM BB, BENJAMIN M, NICK R, et al. Language models are few-shot learners[C]. NIPS'20: Proceedings of the 34th International Conference on Neural Information Processing Systems, Vancouver, B.C., Canada, 2020: 1877-1901.
    [11]
    ZHOU C, LI Q, LI C, et al. A comprehensive survey on pretrained foundation models: A history from BERT to ChatGPT[EB/OL]. [2023-08-01]. https://arxiv.org/abs/2302.09419.
    [12]
    LIU YH, HAN TL, MA SY, et al. Summary of ChatGPT-related research and perspective towards the future of large language models[EB/OL]. [2023-08-01]. https://arxiv.org/abs/2304.01852.
    [13]
    VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all You need[EB/OL]. [2023-08-01]. https://arxiv.org/abs/1706.03762.
    [14]
    韩家炜, MICHELINE K, 裴健, 等. 数据挖掘: 概念与技术[M]. 北京: 机械工业出版社, 2012.

    FAN JW, MICHELINE K, PEI J, et al. Data mining: Concepts and techniques[M]. Beijing: China machine press, 2012.
    [15]
    BALESTRIERO R, IBRAHIM M, SOBAL V, et al. A cookbook of self-supervised learning[EB/OL]. [2023-08-01]. https://arxiv.org/abs/2304.12210.
    [16]
    DU ZX, QIAN YJ, LIU XA, et al. GLM: General language model pretraining with autoregressive blank infilling[C]//Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). Dublin, Ireland. Stroudsburg, PA, USA: Association for Computational Linguistics, 2022: 320-335.
    [17]
    ZHAO WX, ZHOU K, LI JY, et al. A survey of large language models[EB/OL]. [2023-08-01]. https://arxiv.org/abs/2303.18223.
    [18]
    赵朝阳, 朱贵波, 王金桥. ChatGPT给语言大模型带来的启示和多模态大模型新的发展思路[J]. 数据分析与知识发现, 2023, 7(3): 26-35. https://www.cnki.com.cn/Article/CJFDTOTAL-XDTQ202303005.htm

    ZHAO CY, ZHU GB, WANG JQ. The inspiration brought by ChatGPT to LLM and the new development ideas of multi-modal large model[J]. Data Anal Knowl Discov, 2023, 7(3): 26-35. https://www.cnki.com.cn/Article/CJFDTOTAL-XDTQ202303005.htm
    [19]
    陈永伟. 超越ChatGPT: 生成式AI的机遇、风险与挑战[J]. 山东大学学报(哲学社会科学版), 2023(3): 127-143. https://www.cnki.com.cn/Article/CJFDTOTAL-SDZS202303012.htm

    CHEN YW. Beyond ChatGPT: Opportunities, risks, and challenges from generative AI[J]. J Shandong Univ Philos: Soc Sci, 2023(3): 127-143. https://www.cnki.com.cn/Article/CJFDTOTAL-SDZS202303012.htm
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