机构地区:[1]Institute of Basic Research in Clinical Medicine,China Academy of Chinese Medical Sciences [2]Key Laboratory of Advanced Information Science and Network Technology of Beijing,Beijing Jiaotong University [3]Academic Department,China Academy of Chinese Medical Sciences [4]China Academy of Chinese Medical Sciences [5]Personnel Department,China Academy of Chinese Medical Sciences [6]Institute of Acupuncture and Moxibustion,China Academy of Chinese Medical Sciences [7]Scientific Research Department,China Academy of Chinese Medical Sciences
出 处:《Journal of Traditional Chinese Medicine》2014年第5期627-634,共8页中医杂志(英文版)
基 金:Supported by Research on Pattern differentiation of AIDS based on Graph Theroy of National Natural Science Foundation of China(No.81202858);Research on Intervention Evaluation of TCM Health Differentiation of National Key Technology Support Program(No.2012BAI25B02);Research and Development in Digital Information System of Traditional Chinese Medicine of National 863 Program of China(No.2012AA02A609);Acupuncture Efficacy of Gastrointestinal Dysfunction(No.ZZ05003);Acupuncture-point Specialty Analysis based on Image Processing Technology(No.ZZ03090)of Self-selected subject of China Academy of Chinese Medical Sciences;Semantic Recognition of Tongue and Pulse based on Image Content of the Beijing Key Laboratory of Advanced Information Science and Network Technology(No.XDXX1306)
摘 要:OBJECTIVE: To help researchers selecting appropriate data mining models to provide better evidence for the clinical practice of Traditional Chinese Medicine(TCM) diagnosis and therapy.METHODS: Clinical issues based on data mining models were comprehensively summarized from four significant elements of the clinical studies:symptoms, symptom patterns, herbs, and efficacy.Existing problems were further generalized to determine the relevant factors of the performance of data mining models, e.g. data type, samples, parameters, variable labels. Combining these relevant factors, the TCM clinical data features were compared with regards to statistical characters and informatics properties. Data models were compared simultaneously from the view of applied conditions and suitable scopes.RESULTS: The main application problems were the inconsistent data type and the small samples for the used data mining models, which caused the inappropriate results, even the mistake results. These features, i.e. advantages, disadvantages, satisfied data types, tasks of data mining, and the TCM issues, were summarized and compared.CONCLUSION: By aiming at the special features of different data mining models, the clinical doctors could select the suitable data mining models to resolve the TCM problem.OBJECTIVE: To help researchers selecting appropriate data mining models to provide better evidence for the clinical practice of Traditional Chinese Medicine(TCM) diagnosis and therapy.METHODS: Clinical issues based on data mining models were comprehensively summarized from four significant elements of the clinical studies:symptoms, symptom patterns, herbs, and efficacy.Existing problems were further generalized to determine the relevant factors of the performance of data mining models, e.g. data type, samples, parameters, variable labels. Combining these relevant factors, the TCM clinical data features were compared with regards to statistical characters and informatics properties. Data models were compared simultaneously from the view of applied conditions and suitable scopes.RESULTS: The main application problems were the inconsistent data type and the small samples for the used data mining models, which caused the inappropriate results, even the mistake results. These features, i.e. advantages, disadvantages, satisfied data types, tasks of data mining, and the TCM issues, were summarized and compared.CONCLUSION: By aiming at the special features of different data mining models, the clinical doctors could select the suitable data mining models to resolve the TCM problem.
关 键 词:Medicine Chinese traditional Biomedi-cal research Data mining Model Comparison anal-ysis
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