A Weighted Voting Approach for Traditional Chinese Medicine Formula Classification Using Large Language Models: Algorithm Development and Validation Study.

Journal: JMIR medical informatics
Published Date: Jul 24, 2025

Abstract

BACKGROUND: Several clinical cases and experiments have demonstrated the effectiveness of traditional Chinese medicine (TCM) formulas in treating and preventing diseases. These formulas contain critical information about their ingredients, efficacy, and indications. Classifying TCM formulas based on this information can effectively standardize TCM formulas management, support clinical and research applications, and promote the modernization and scientific use of TCM. To further advance this task, TCM formulas can be classified using various approaches, including manual classification, machine learning, and deep learning. Additionally, large language models (LLMs) are gaining prominence in the biomedical field. Integrating LLMs into TCM research could significantly enhance and accelerate the discovery of TCM knowledge by leveraging their advanced linguistic understanding and contextual reasoning capabilities.

Authors

  • Zhe Wang
    Department of Pathology, The Eighth Affiliated Hospital, Sun Yat-sen University, Shenzhen 518033, China.
  • Keqian Li
    School of Medical Information, Changchun University of Chinese Medicine, Changchun, China.
  • Suyuan Peng
    Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA; The Second Clinical College Guangzhou University of Chinese Medicine, China.
  • Lihong Liu
    State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Xiaolin Yang
    Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Beijing, China. yangxl@pumc.edu.cn.
  • Keyu Yao
    Institute of Information on Traditional Chinese Medicine, China Academy of Chinese Medical Sciences, No 16, Nanxiao Street, Dongzhimen, Beijing, 100010, China, 86 010 64089639.
  • Heinrich Herre
    Institute for Medical Informatics, Statistics and Epidemiology (IMISE), University of Leipzig, Leipzig, Germany.
  • Yan Zhu
    Department of Chemistry, Xixi Campus, Zhejiang University, Hangzhou, 310028, China. Electronic address: zhuyan@zju.edu.cn.

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