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
Bibliographic record
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. 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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".