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：Xiaojie Jin, Y Wang, Jiarui Wang, Qian Gao, Yuhan Huang, Lingyu Shao, Jiali Zhao, J. Li, Ling Li, Zhiming Zhang, Shuyan Li, Yongqi Liu · 发表于：JMIR Medical Informatics · 年份：2025 · DOI：10.2196/64725 · 被引用次数：5 · 研究领域：Traditional Chinese Medicine Studies、Machine Learning in Healthcare、Phonocardiography and Auscultation Techniques

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 pneu...