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    Diabetes Risk Data Mining Method Based on Electronic Medical Record Analysis. Liu Yang,Yu Zhaoxiang,Yang Yunlong Journal of healthcare engineering In today's society, the development of information technology is very rapid, and the transmission and sharing of information has become a development trend. The results of data analysis and research are gradually applied to various fields of social development, structured analysis, and research. Data mining of electronic medical records in the medical field is gradually valued by researchers and has become a major work in the medical field. In the course of clinical treatment, electronic medical records are edited, including all personal health and treatment information. This paper mainly introduces the research of diabetes risk data mining method based on electronic medical record analysis and intends to provide some ideas and directions for the research of diabetes risk data mining method. This paper proposes a research strategy of diabetes risk data mining method based on electronic medical record analysis, including data mining and classification rule mining based on electronic medical record analysis, which are used in the research experiment of diabetes risk data mining method based on electronic medical record analysis. The experimental results in this paper show that the average prediction accuracy of the decision tree is 91.21%, and the results of the training set and the test set are similar, indicating that there is no overfitting of the training set. 10.1155/2021/6678526
    Data mining in clinical big data: the frequently used databases, steps, and methodological models. Wu Wen-Tao,Li Yuan-Jie,Feng Ao-Zi,Li Li,Huang Tao,Xu An-Ding,Lyu Jun Military Medical Research Many high quality studies have emerged from public databases, such as Surveillance, Epidemiology, and End Results (SEER), National Health and Nutrition Examination Survey (NHANES), The Cancer Genome Atlas (TCGA), and Medical Information Mart for Intensive Care (MIMIC); however, these data are often characterized by a high degree of dimensional heterogeneity, timeliness, scarcity, irregularity, and other characteristics, resulting in the value of these data not being fully utilized. Data-mining technology has been a frontier field in medical research, as it demonstrates excellent performance in evaluating patient risks and assisting clinical decision-making in building disease-prediction models. Therefore, data mining has unique advantages in clinical big-data research, especially in large-scale medical public databases. This article introduced the main medical public database and described the steps, tasks, and models of data mining in simple language. Additionally, we described data-mining methods along with their practical applications. The goal of this work was to aid clinical researchers in gaining a clear and intuitive understanding of the application of data-mining technology on clinical big-data in order to promote the production of research results that are beneficial to doctors and patients. 10.1186/s40779-021-00338-z
    [Application and progress of data mining in study of compatibility law of traditional Chinese medicine]. Liu Meng-Ling,Zhang Xin-You,Ding Liang,Pan Shu-Mao,Wu Di-Yao,Li Xiu-Yun Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica 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. 10.19540/j.cnki.cjcmm.20210303.501