[CiteSpace knowledge mapping of traditional Chinese medicine in treatment of premature ovarian failure].

This study aimed to investigate the research trend of traditional Chinese medicine(TCM) against premature ovarian fai-lure(POF) from 1989 to 2021 by bibliometrics and explore the research status, research hotspots, and advances in international co-operation, knowledge structure, and active topics.The research articles on POF published from database inception to December 28, 2021, were retrieved from Web of Science and China National Knowledge Infrastructure(CNKI) and visually analyzed for countries, journals, authors, institutions, and keywords by CiteSpace 5.8.R3.A total of 1 468 articles were included, including 217 in English and 1 251 in Chinese.Since 1989, there has been an overall upward trend in the number of articles, with China serving as the main contributor.The core authors of Chinese articles are from a cooperative team represented by FENG Yi-xuan, REN Yu-lan, LING Le-le, and TENG Xiu-xiang.BETTERLE C is the author with the highest number of published articles in this international research field.The articles are mainly published by TCM journals and universities, and Human Reproduction accounts for the highest proportion of publications in the international research(11 articles, 5.07%).In the retrieved research articles, the research contents mainly focus on the treatment methods, research methods, and mechanism of action of TCM in the treatment of POF, where "Zuogui Pills" "gene" "cell" "model" "expression", etc.are the current research hotspots. "Acupuncture" "data mining" "systematic review" "oxidative stress" "activation" may be the potential topics in the follow-up research development.Future development should focus on the scientific interpretation and analysis of the theory and practice of TCM by modern scientific and technological methods.The research on informatization, digitization, and knowledge of TCM theory and practice is pivotal to promoting the internationalization and modernization of TCM, which can help researchers explore new directions for future research and identify new perspectives for potential collaboration in the field.

[Application and progress of data mining in study of compatibility law of traditional Chinese medicine].

Data mining is an important method to obtain the key information from a large amount of data, and it is widely applied in the research on the modernization of traditional Chinese medicine(TCM). The compatibility law of herbs is a key issue in the research of TCM prescriptions. This reflects the flexibility and effectiveness of TCM prescriptions, and it is also a crucial link to the development of TCM modernization. Therefore, it is the core purpose of the research on TCM prescriptions to find the compatibility law of herbs and clarify the scientific connotation. Data mining, as an effective method and an important approach, has formed a standardized system in the research of compatibility law of herbs, which can reveal the relationship between different Chinese herbs and summarize the internal rules in compatibility. Two hundred and twenty two effective papers were sorted out and categorized in this article. The results showed that data mining was mainly applied in finding the core Chinese herb pairs, summarizing the utility and attributes of TCM prescriptions, revealing the relationship between prescriptions, Chinese herbs and syndromes, finding the optimal dose of Chinese herbs, and producing the new prescriptions. The problems of data mining in research of herbs compatibility rules were summarized, and its development and trend in current researches were discussed in this article to provide useful references for the in-depth study of data mining in the compatibility law of Chinese herbs.

Application of an extreme learning machine network with particle swarm optimization in syndrome classification of primary liver cancer.

OBJECTIVE: By optimizing the extreme learning machine network with particle swarm optimization, we established a syndrome classification and prediction model for primary liver cancer (PLC), classified and predicted the syndrome diagnosis of medical record data for PLC and compared and analyzed the prediction results with different algorithms and the clinical diagnosis results. This paper provides modern technical support for clinical diagnosis and treatment, and improves the objectivity, accuracy and rigor of the classification of traditional Chinese medicine (TCM) syndromes. METHODS: From three top-level TCM hospitals in Nanchang, 10,602 electronic medical records from patients with PLC were collected, dating from January 2009 to May 2020. We removed the electronic medical records of 542 cases of syndromes and adopted the cross-validation method in the remaining 10,060 electronic medical records, which were randomly divided into a training set and a test set. Based on fuzzy mathematics theory, we quantified the syndrome-related factors of TCM symptoms and signs, and information from the TCM four diagnostic methods. Next, using an extreme learning machine network with particle swarm optimization, we constructed a neural network syndrome classification and prediction model that used "TCM symptoms + signs + tongue diagnosis information + pulse diagnosis information" as input, and PLC syndrome as output. This approach was used to mine the nonlinear relationship between clinical data in electronic medical records and different syndrome types. The accuracy rate of classification was used to compare this model to other machine learning classification models. RESULTS: The classification accuracy rate of the model developed here was 86.26%. The classification accuracy rates of models using support vector machine and Bayesian networks were 82.79% and 85.84%, respectively. The classification accuracy rates of the models for all syndromes in this paper were between 82.15% and 93.82%. CONCLUSION: Compared with the case of data processed using traditional binary inputs, the experiment shows that the medical record data processed by fuzzy mathematics was more accurate, and closer to clinical findings. In addition, the model developed here was more refined, more accurate, and quicker than other classification models. This model provides reliable diagnosis for clinical treatment of PLC and a method to study of the rules of syndrome differentiation and treatment in TCM.