Title | ||
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Rule-Based Text Mining Of Traditional Chinese Medicine Patterns With Chinese Herbal Medicines And Formulae On Hypertension |
Abstract | ||
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Through several thousands years of clinical research and theoretical thoughts, traditional Chinese medicine (TCM) has accumulated rich experience on hypertension. However, the usage of Chinese herbal medicines (CHMs) in formulae is flexible in TCM clinical practice according to pattern differentiation. So, it is important to get the composition rules of Chinese herbal medicines through literatures. Based on the keyword list of Chinese herbal medicine, through the keyword filtering skill, we got the lists of Chinese herbal medicines. However, for Chinese herbal medicine, they are not only mentioned in the plain format of herb names, but also densely described in the form of formulae. As formulae are composed by Chinese herbal medicines according the theory of traditional Chinese medicine, so it is necessary to filtering them out and de-compose them back into specified Chinese herbal medicines. In this study, take hypertension for example, we explored the composition-rules of Chinese herbal medicines and the network of TCM pattern with them. Networks of TCM patterns and CHMs which are most frequently used in hypertension treatment are built-up and analyzed, some regularities are obtained in treating hypertension from 175011 records of literature. And this method could provide useful help for TCM clinical application and Chinese medicine research. |
Year | DOI | Venue |
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2013 | 10.1109/BIBM.2013.6732708 | 2013 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | Field | DocType |
Chinese herbal medicine, pattern, decoction, traditional Chinese medicine, hypertension | Rule-based system,Computer science,Clinical Practice,Traditional Chinese medicine,Natural language processing,Artificial intelligence,Decoction,Machine learning,Patient treatment | Conference |
Volume | Issue | ISSN |
null | null | 2156-1125 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
14 |
Name | Order | Citations | PageRank |
---|---|---|---|
Hongmei Zhou | 1 | 0 | 1.35 |
Jing Yang | 2 | 0 | 0.68 |
Jinrui Guo | 3 | 0 | 1.01 |
Yahong Wang | 4 | 0 | 1.01 |
Guang Zheng | 5 | 28 | 10.72 |
Hongtao Guo | 6 | 29 | 9.71 |
Yong Tan | 7 | 0 | 0.34 |
Xiaoxia Ren | 8 | 165 | 8.37 |
Rongfen Dong | 9 | 0 | 1.35 |
Jinrong Zhang | 10 | 0 | 1.35 |
Zhaoli Cui | 11 | 0 | 1.01 |
Aiping Lu | 12 | 0 | 0.68 |
Miao Jiang | 13 | 63 | 12.07 |
Yaoxian Wang | 14 | 0 | 2.70 |