文献类型：期刊文献

英文题名：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

作者：Jin, Xiaojie[1,2];Wang, Yanru[1];Wang, Jiarui[3];Gao, Qian[2];Huang, Yuhan[1];Shao, Lingyu[4];Zhao, Jiali[1];Li, Jintian[1];Li, Ling[1];Zhang, Zhiming[5];Li, Shuyan[4];Liu, Yongqi[1]

第一作者：靳晓杰

通信作者：Liu, YQ[1]

机构：[1]Gansu Univ Chinese Med, Key Lab Dunhuang Med, Minist Educ, Dingxi East Rd 35th, Lanzhou 730000, Peoples R China;[2]Gansu Univ Chinese Med, Coll Pharm, Lanzhou, Peoples R China;[3]Xuzhou Med Univ, Affiliated Hosp 2, Xuzhou, Peoples R China;[4]Xuzhou Med Univ, Sch Med Informat & Engn, Xuzhou, Peoples R China;[5]Gansu Prov Hosp Tradit Chinese Med, Lanzhou, Peoples R China

第一机构：甘肃中医药大学

通信机构：[1]corresponding author), Gansu Univ Chinese Med, Key Lab Dunhuang Med, Minist Educ, Dingxi East Rd 35th, Lanzhou 730000, Peoples R China.|[10735]甘肃中医药大学;

年份：2025

卷号：13

外文期刊名：JMIR MEDICAL INFORMATICS

收录：;Scopus(收录号：2-s2.0-105014798610);WOS:【SCI-EXPANDED(收录号:WOS:001544213500002)】；

基金：This study was supported by the Major Science and Technology Project of Gansu Province (grant 22ZD1FA001), the Provincial University Industry Support Project in Gansu (grant 2020C-36), the Young Elite Scientists Sponsorship Program by the Gansu Association for Science and Technology (grant GXH20210611-02), and the 2023 Longyuan Youth Innovation and Entrepreneurship Talent Project (Gan Group Tong Zi [2023] No. 20).

语种：英文

外文关键词：syndrome differentiation; traditional Chinese medicine; viral pneumonia; dialectical treatment; machine learning; Chinese medicine; pneumonia; lungs; diagnostic model; retrospective study; artificial intelligence; China; model training; performance evaluation; laboratory tests; modern medicine

摘要：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. Objective: This study aims to construct a diagnostic model for differentiating cold and hot syndromes in viral pneumonia by integrating TCM and modern medical features using machine learning methods. Methods: The application of 8 machine learning algorithms (gradient boosting machine [GBM], logistic regression, random forest, extreme gradient boosting [XGB], light gradient boosting machine [LGB], ridge regression, least absolute shrinkage and selection operator, and support vector machine) generated and validated (both internally and externally) a model for differentiating cold and hot syndromes in viral pneumonia, based on clinical data from 1484 patient samples collected at 2 medical centers between 2021 and 2022. Results: The GBM model, which combines TCM and modern medicine features, outperformed models using only TCM features or only modern medicine features in distinguishing cold and hot syndromes in patients with viral pneumonia. The optimal discrimination model comprised 13 optimal features (temperature, red cell distribution width-SD, creatinine, total bilirubin, globulin, C-reactive protein, unconjugated bilirubin, white blood cell, neutrophil percentage, aspartate transaminase/alanine transaminase, total cholesterol, thrombocytocrit, and age) and the GBM algorithm, achieving an area under the curve (AUC) of 0.7788. Under internal and external testing, the AUCs were 0.7645 and 0.8428, respectively. Moreover, significant differences were observed between the cold and hot syndrome groups in temperature (P=.02), red cell distribution width-SD (P<.001), neutrophil percentage (P=.01), total cholesterol (P=.003), thrombocytocrit (P<.001), and age (P<.001). Conclusions: This pioneering study integrates the theory of TCM cold and hot syndromes with modern laboratory-based tests through machine learning. The developed model offers a novel approach for differentiating cold and hot syndromes in viral pneumonia, enabling practitioners to identify the syndrome quickly and efficiently, thereby supporting more informed clinical decision-making. Additionally, this research provides new insights into the modernization and scientific interpretation of TCM syndrome differentiation.