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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.
RESUMO
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.
