A Machine Learning Approach to Differentiate Cold and Hot Syndrome in Viral Pneumonia Integrating Traditional Chinese Medicine and Modern Medicine: Machine Learning Model Development and Validation.

Journal: JMIR medical informatics
Published Date: Jul 16, 2025

Abstract

BACKGROUND: Syndrome differentiation in traditional Chinese medicine (TCM) is an ancient principle that guides disease diagnosis and treatment. Among these, the cold and hot syndromes play a crucial role in identifying the nature of the disease and guiding the treatment of viral pneumonia. However, differentiating between cold and hot syndromes is often considered esoteric. Machine learning offers a promising avenue for clinicians to identify these syndromes more accurately, thereby supporting more informed clinical decision-making in the treatment.

Authors

  • Xiaojie Jin
    Key Laboratory of Dunhuang Medicine, Ministry of Education, Gansu University of Chinese Medicine, Dingxi East Road, 35th, Lanzhou, 730000, China, 86 13919019578.
  • Yanru Wang
    State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing, 210023, Jiangsu, China.
  • Jiarui Wang
    School of Mathematics, China University of Mining and Technology, Xuzhou, 221116, China. Electronic address: karsuiwang@gmail.com.
  • Qian Gao
    Department of Obstetrics, The Third Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510630, China.
  • Yuhan Huang
    College of Life Science and Oceanography, Shenzhen University, Shenzhen 518060, China.
  • Lingyu Shao
    School of Medical Informatics and Engineering, Xuzhou Medical University, Xuzhou 221004, Jiangsu, China. Electronic address: 303109110909@stu.xzhmu.edu.cn.
  • Jiali Zhao
    Key Laboratory of Dunhuang Medicine, Ministry of Education, Gansu University of Chinese Medicine, Dingxi East Road, 35th, Lanzhou, 730000, China, 86 13919019578.
  • Jintian Li
    CAS Key Laboratory of Receptor Research, Stake Key Laboratory of Drug Research; Drug Discovery and Design Center, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai, 201203, China.
  • Ling Li
    College of Communication Engineering, Jilin University, Changchun, Jilin China.
  • Zhiming Zhang
    Shenzhen Prevention and Treatment Center for Occupational Diseases, Shenzhen 518020, China.
  • Shuyan Li
  • Yongqi Liu
    Gansu University Key Laboratory for Molecular Medicine & Chinese Medicine Prevention and Treatment of Major Diseases, Gansu University of Chinese Medicine, Lanzhou, China; Key Laboratory of Dunhuang Medical and Transformation, Ministry of Education of The People's Republic of China, Gansu University of Chinese Medicine, Lanzhou, China. Electronic address: liuyongqi73@163.com.

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