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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.

Liver Stagnation and Spleen Deficiency (LSSD) is a Chinese Medicine (CM) pattern commonly observed in gastrointestinal (GI) diseases, yet its biological nature remains unknown. This limits the global use of CM medications for treating GI diseases. Recent studies emphasize the role of gut microbiota and their metabolites in the pathogenesis and treatment of LSSD-associated GI diseases. There is increasing evidence supporting that an altered gut microbiome in LSSD patients or animals contributes to GI and extra-intestinal symptoms and affects the effectiveness of CM therapies. The gut microbiota is considered to be an essential component of the biological basis of LSSD. This study aims to provide an overview of existing research findings and gaps for the pathophysiological study of LSSD from the gut microbiota perspective in order to understand the relationship between the CM pattern and disease progression and to optimize CM-based diagnosis, prevention, and therapy. [ABSTRACT FROM AUTHOR]

On the basis of the expression of specific genes, breast cancer is classified into four molecular types: Luminal A, Luminal B, human epidermal growth factor receptor 2 (HER-2)-positive, and triple negative. By summarizing and analyzing the studies published in the past decade, including the correlation between molecular types of breast cancer and traditional Chinese medicine(TCM) patterns, as well as the connection between the TCM constitution and clinical characteristics of different molecular types of breast cancer, this study reveals that there are differences in TCM patterns between different types of breast cancer. Notably, the pattern of liver depression and qi stagnation and the pattern of thoroughfare-controlling vessel disharmony are common contributors to the development of Luminal A and B breast cancer. In patients with HER-2-positive breast cancer, the main pathogenic mechanism is that wind, an exterior pathogen in Chinese medicine, complicated with phlegm and static blood, flows through the channels. In patients with triple-negative breast cancer, toxin, phlegm, and static blood are cemented in the channels. Therefore, in TCM-based diagnosis and treatment of breast cancer, it is necessary to make targeted changes according to the pathogenesis characteristics of different molecular types based on the core treatment principle of soothing the liver and invigorating the spleen, paying attention to the balance of qi, blood, and yin and yang and achieving accurate TCM differentiation and treatment with the integration of disease differentiation, molecular typing, and pattern differentiation. [ABSTRACT FROM AUTHOR]

This literary review delves into the two principal approaches utilized in Chinese medicine to treat warm diseases: the Wei-qi-ying-blood pattern identification and the sanjiao pattern differentiation. These methods were developed primarily to manage epidemic and infectious ailments, and have become particularly significant in light of the worldwide pandemic that has impacted the globe over the past four years. The review analyzes and differentiates each sub-pattern of the Wei-qi-ying-blood and sanjiao approaches, followed by a comparison between the two methods of predominant pattern differentiation. Ultimately, the clinical relevance of these approaches is thoroughly examined, underscoring the critical role they play in effectively treating warm diseases. [ABSTRACT FROM AUTHOR]

Background: China has become the country with the largest number of people with type 2 diabetes mellitus (T2DM), and Chinese medicine (CM) has unique advantages in preventing and treating T2DM, while accurate pattern differentiation is the guarantee for proper treatment. Objective: The establishment of the CM pattern differentiation model of T2DM is helpful to the pattern diagnosis of the disease. At present, there are few studies on dampness-heat pattern differentiation models of T2DM. Therefore, we establish a machine learning model, hoping to provide an efficient tool for the pattern diagnosis of CM for T2DM in the future. Methods: A total of 1021 effective samples of T2DM patients from ten CM hospitals or clinics were collected by a questionnaire including patients' demographic and dampness-heat-related symptoms and signs. All information and the diagnosis of the dampness-heat pattern of patients were completed by experienced CM physicians at each visit. We applied six machine learning algorithms (Artificial Neural Network [ANN], K-Nearest Neighbor [KNN], Naïve Bayes [NB], Support Vector Machine [SVM], Extreme Gradient Boosting [XGBoost] and Random Forest [RF]) and compared their performance. And then we also utilized Shapley additive explanation (SHAP) method to explain the best performance model. Results: The XGBoost model had the highest AUC (0.951, 95% CI 0.925–0.978) among the six models, with the best sensitivity, accuracy, F1 score, negative predictive value, and excellent specificity, precision, and positive predictive value. The SHAP method based on XGBoost showed that slimy yellow tongue fur was the most important sign in dampness-heat pattern diagnosis. The slippery pulse or rapid-slippery pulse, sticky stool with ungratifying defecation also performed an important role in this diagnostic model. Furthermore, the red tongue acted as an important tongue sign for the dampness-heat pattern. Conclusion: This study constructed a dampness-heat pattern differentiation model of T2DM based on machine learning. The XGBoost model is a tool with the potential to help CM practitioners make quick diagnosis decisions and contribute to the standardization and international application of CM patterns.

Gastric cancer (GC) is the fifth most common type of cancer and the third leading cause of death due to cancer worldwide. The gastric mucosa often undergoes many years of precancerous lesions of gastric cancer (PLGC) stages before progressing to gastric malignancy. Unfortunately, there are no effective Western drugs for patients with PLGC. In recent years, traditional Chinese medicine (TCM) has been proven effective in treating PLGC. Classical TCM formulas and chemical components isolated from some Chinese herbal medicines have been administered to treat PLGC, and the main advantage is their comprehensive intervention with multiple approaches and multiple targets. In this review, we focus on recent studies using TCM treatment for PLGC, including clinical observations and experimental research, with a focus on targets and mechanisms of drugs. This review provides some ideas and a theoretical basis for applying TCM to treat PLGC and prevent GC.

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., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in the paper., (© 2024 The Authors.)

Background: China has become the country with the largest number of people with type 2 diabetes mellitus (T2DM), and Chinese medicine (CM) has unique advantages in preventing and treating T2DM, while accurate pattern differentiation is the guarantee for proper treatment., Objective: The establishment of the CM pattern differentiation model of T2DM is helpful to the pattern diagnosis of the disease. At present, there are few studies on dampness-heat pattern differentiation models of T2DM. Therefore, we establish a machine learning model, hoping to provide an efficient tool for the pattern diagnosis of CM for T2DM in the future., Methods: A total of 1021 effective samples of T2DM patients from ten CM hospitals or clinics were collected by a questionnaire including patients' demographic and dampness-heat-related symptoms and signs. All information and the diagnosis of the dampness-heat pattern of patients were completed by experienced CM physicians at each visit. We applied six machine learning algorithms (Artificial Neural Network [ANN], K-Nearest Neighbor [KNN], Naïve Bayes [NB], Support Vector Machine [SVM], Extreme Gradient Boosting [XGBoost] and Random Forest [RF]) and compared their performance. And then we also utilized Shapley additive explanation (SHAP) method to explain the best performance model., Results: The XGBoost model had the highest AUC (0.951, 95% CI 0.925-0.978) among the six models, with the best sensitivity, accuracy, F1 score, negative predictive value, and excellent specificity, precision, and positive predictive value. The SHAP method based on XGBoost showed that slimy yellow tongue fur was the most important sign in dampness-heat pattern diagnosis. The slippery pulse or rapid-slippery pulse, sticky stool with ungratifying defecation also performed an important role in this diagnostic model. Furthermore, the red tongue acted as an important tongue sign for the dampness-heat pattern., Conclusion: This study constructed a dampness-heat pattern differentiation model of T2DM based on machine learning. The XGBoost model is a tool with the potential to help CM practitioners make quick diagnosis decisions and contribute to the standardization and international application of CM patterns., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors. Published by Elsevier Ltd.)

Gastric cancer (GC) is the fifth most common type of cancer and the third leading cause of death due to cancer worldwide. The gastric mucosa often undergoes many years of precancerous lesions of gastric cancer (PLGC) stages before progressing to gastric malignancy. Unfortunately, there are no effective Western drugs for patients with PLGC. In recent years, traditional Chinese medicine (TCM) has been proven effective in treating PLGC. Classical TCM formulas and chemical components isolated from some Chinese herbal medicines have been administered to treat PLGC, and the main advantage is their comprehensive intervention with multiple approaches and multiple targets. In this review, we focus on recent studies using TCM treatment for PLGC, including clinical observations and experimental research, with a focus on targets and mechanisms of drugs. This review provides some ideas and a theoretical basis for applying TCM to treat PLGC and prevent GC. [Display omitted] • This review summarized the mainly classification of syndrome differentiation of PLGC. • This review summarized the therapy of Chinese medicine formulas or components on PLGC. • This review listed the pharmacological mechanisms of the TCM for PLGC. [ABSTRACT FROM AUTHOR]