Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Jul 25, 2024
Date Accepted: May 9, 2025
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
ABSTRACT
Background:
Syndromes differentiation in traditional Chinese medicine (TCM) is an ancient principle guiding disease diagnosis and treatment, contrasting with modern medicine. The cold and hot syndrome, two essential conditions of the Eight Principle Syndromes, play a crucial role in identifying the nature of various diseases, including viral pneumonia. However, the profound theory of syndrome differentiation is often considered esoteric. Recently, the increasing application of artificial intelligence (AI) in TCM provides a modern approach to interpret the scientific connotation of TCM diagnosis.
Objective:
This study aimed to construct a diagnostic model for differentiating the cold and hot syndrome in viral pneumonia by integrating TCM symptoms and the laboratory-based tests using machine learning methods.
Methods:
A retrospective study was conducted using clinical data of viral pneumonia patients from the Second People's Hospital of Lanzhou City, China. Eight commonly used machine learning algorithms were used for model training and performance evaluation.
Results:
Machine learning algorithms based on integrated TCM and modern medicine features outperformed models solely using TCM features in distinguishing the cold and hot syndrome in patients with viral pneumonia. The Gradient Boosting Machine (GBM) method achieved the best performance with an area under the curve (AUC) of 0.7788. Thirteen clinical features, including body temperature, red blood cell distribution width-standard deviation (RDW-SD), creatinine (CREA), total bilirubin (TBIL), globulin (GLO), C-reactive protein (CRP), etc., were identified as the key indicators for syndrome differentiation.
Conclusions:
This pioneering study to integrated TCM cold and hot syndrome theory with modern laboratory-based tests using machine learning. The established model provides a novel method for TCM cold and hot syndorme differentiation in viral pneumonia, assisting practitioners in comprehensive diagnosis and identifying effective Chinese herbal medicine treatments. This research offers new insights into the modernization and scientific interpretation of TCM syndrome differentiation.
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