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Purpose: This retrospective cohort study aimed to analyze the relationship between tongue color and coronary artery stenosis severity in 282 patients after underwent coronary angiography. Methods: A retrospective cohort study was conducted to collect data from patients who underwent coronary angiography in the Department of Cardiology, Shanghai Jiading District Central Hospital from October 1, 2023 to January 15, 2024. All patients were divided into four various stenosis groups. The tongue images of each patient was normalized captured, tongue body (TC_) and tongue coating (CC_) data were converted into RGB and HSV model parameters using SMX System 2.0. Four supervised machine learning classifiers were used to establish a coronary artery stenosis grading prediction model, including random forest (RF), logistic regression, and support vector machine (SVM). Accuracy, precision, recall, and F1 score were used as classification indicators to evaluate the training and validation performance of the model. SHAP values were furthermore used to explore the impacts of features. Results: This study finally included 282 patients, including 164 males (58.16%) and 118 females (41.84%). 69 patients without stenosis, 70 patients with mild stenosis, 65 patients with moderate stenosis, and 78 patients with severe stenosis. Significant differences of tongue parameters were observed in the four groups [TC_R (P = 0.000), TC_G (P = 0.003), TC_H (P = 0.001) and TC_S (P = 0.024),CC_R (P = 0.006), CC_B (P = 0.023) and CC_S (P = 0.001)]. The SVM model had the highest predictive ability, with AUC values above 0.9 in different stenosis groups, and was particularly good at identifying mild and severe stenosis (AUC = 0.98). SHAP value showed that high values of TC_RIGHT_R, low values of CC_LEFT_R were the most impact factors to predict no coronary stenosis; high CC_LEFT_R and low TC_ROOT_H for mild coronary stenosis; low TC_ROOT_R and CC_ROOT_B for moderate coronary stenosis; high CC_RIGHT_G and low TC_ROOT_H for severe coronary stenosis. Conclusion: Tongue color parameters can provide a reference for predicting the degree of coronary artery stenosis. The study provides insights into the potential application of tongue color parameters in predicting coronary artery stenosis severity. Future research can expand on tongue features, optimize prediction models, and explore applications in other cardiovascular diseases.
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Purpose: To analyse the current status of outcome indicators in randomised controlled trials (RCTs) of traditional Chinese medicine (TCM) for the treatment of coronary heart disease (CHD) with deficiency of qi and yin, and to provide a basis for constructing a core indicator set (COS) for TCM treatment of CHD. Methods: We searched the database of China National Knowledge Infrastructure (CNKI), PubMed,etc. 8 databases in the last 5 years. RCTs of TCM for CHD included in core journals were evaluated for the risk of bias of the included studies, and the current status of the selection of outcome indicators was statistically analysed. Results: A total of 39 RCTs with a sample size of 44~398 cases were included, and 164 outcome indicators were reported, with a frequency of 383 applications. The outcome indicators were categorised into 6 indicator domains according to their functional attributes, which were, in descending order, safety indicators, physicochemical examination, effective rate, economic assessment, disease evidence score, and quality of life. The top 3 indicators in terms of frequency of application of outcome indicators were safety indicators, physical and chemical examination indicators, and efficiency, among which electrocardiogram, inflammation indicators, and clinical efficacy were the most frequently used; there were many different types of measurement tools for outcome indicators, among which total efficiency and TCM symptom points were the most frequently used; the time point of measurement was not the same. Conclusion: The RCTs of TCM for CHD in the last 5 years have many shortage in the selection of outcome indicators, and should actively promote the construction of the COS of TCM for CHD.
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Background: Traditional Chinese Medicine (TCM) offers individualized treatment for Polycystic Ovary Syndrome (PCOS) through pattern differentiation, but the subjectivity of TCM diagnoses can lead to inconsistent outcomes. Integrating machine learning (ML) offers an objective basis to support TCM diagnoses. This study aims to evaluate various feature selection techniques and multi-label ML algorithms to develop an effective predictive model for classifying TCM patterns in PCOS patients, thereby enhancing diagnostic standardization and treatment personalization. Methods: The study utilized a dataset comprising 432 patients with PCOS, exhibiting one or more of five TCM patterns. Feature selection began with Variance Thresholding (VT), followed by a comparison of five advanced techniques: Statistical Analysis Test, Recursive Feature Elimination with Cross-Validation (RFECV), Least Absolute Shrinkage and Selection Operator Regression, BorutaShap, and ReliefF. To ascertain the most effective model for predicting PCOS TCM patterns, four ML algorithms-Support Vector Machine, Logistic Regression, Extreme Gradient Boosting (XGBoost), and Artificial Neural Networks-were evaluated against the identified feature set. Results: VT reduced the feature count from 224 to 174. RFECV emerged as the most effective feature selection method, identifying 67 key features. XGBoost emerged as the top-performing model, demonstrating superior testing accuracy (0.7870), F1 score (0.9519), and Hamming loss (0.0481) with RFECV-optimized features. Conclusions: The RFECV-XGBoost model proved effective for classifying TCM patterns in PCOS. It emphasizes the necessity of precise feature selection and the significant capabilities of ML in advancing TCM pattern diagnostics, marking a significant step toward enhancing precise and personalized healthcare in biomedical studies.
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Aim of the Study: This study analyzes research on TCM formulae in CHD over the past 30 years, using VOSviewer and CiteSpace. It aims to highlight key trends and hotspots in the field. Materials and Methods: The core database of Web of Science was collected, and the search time range was from the establishment of the database to the present (August 2023) for the literature related to the study of TCM prescriptions in CHD, and the information on the number of literature, countries, journals, authors, institutions, keywords were summarized by applying the software VOSviewer and CiteSpace. Results: A total of 135 kinds of literature were included. The number of published journal papers on research on TCM therapeutic formulae for CHD showed an upward trend; China was the most prolific country in this field; the largest number of papers were published in Evid Based Complement Alternat Med, MEDICINE; the average number of citations for authors and institutional analysis revealed that Xu Hao of China Academy of Traditional Chinese Medicine, Mao Jingyuan of Tianjin University of Traditional Chinese Medicine, and Shang Hongcai of Beijing University of Traditional Chinese Medicine constituted the core team of researchers studying the study of TCM formulae for CHD; the keyword analysis suggests that there are mainly 42 specifically named TCM formulae for the treatment of CHD, which are classified into a total of 7 major categories, and the research direction is mainly in the clinical efficacy study of different TCM therapeutic formulae and other aspects. Conclusion: This study shows that there are more types of TCM therapeutic formulae for CHD, and the related research has a good prospect. It is foreseeable that more relevant research results will rely on the study of network pharmacology, signalling pathways, and action targets of TCM therapeutic formulae.
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BACKGROUND: Patients with Polycystic ovary syndrome (PCOS) experienced endocrine disorders that may present vascular function changes. This study aimed to classify and predict PCOS by radial pulse wave parameters using machine learning (ML) methods and to provide evidence for objectifying pulse diagnosis in traditional Chinese medicine (TCM). METHODS: A case-control study with 459 subjects divided into a PCOS group and a healthy (non-PCOS) group. The pulse wave parameters were measured and analyzed between the two groups. Seven supervised ML classification models were applied, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Decision Trees, Random Forest, Logistic Regression, Voting, and Long Short Term Memory networks (LSTM). Parameters that were significantly different were selected as input features and stratified k-fold cross-validations training was applied to the models. RESULTS: There were 316 subjects in the PCOS group and 143 subjects in the healthy group. Compared to the healthy group, the pulse wave parameters h3/h1 and w/t from both left and right sides were increased while h4, t4, t, As, h4/h1 from both sides and right t1 were decreased in the PCOS group (P < 0.01). Among the ML models evaluated, both the Voting and LSTM with ensemble learning capabilities, demonstrated competitive performance. These models achieved the highest results across all evaluation metrics. Specifically, they both attained a testing accuracy of 72.174% and an F1 score of 0.818, their respective AUC values were 0.715 for the Voting and 0.722 for the LSTM. CONCLUSION: Radial pulse wave signal could identify most PCOS patients accurately (with a good F1 score) and is valuable for early detection and monitoring of PCOS with acceptable overall accuracy. This technique can stimulate the development of individualized PCOS risk assessment using mobile detection technology, furthermore, gives physicians an intuitive understanding of the objective pulse diagnosis of TCM. TRIAL REGISTRATION: Not applicable.