Title
Rule-Based Text Mining Of Traditional Chinese Medicine Patterns With Chinese Herbal Medicines And Formulae On Hypertension
Abstract
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
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 Zhou101.35
Jing Yang200.68
Jinrui Guo301.01
Yahong Wang401.01
Guang Zheng52810.72
Hongtao Guo6299.71
Yong Tan700.34
Xiaoxia Ren81658.37
Rongfen Dong901.35
Jinrong Zhang1001.35
Zhaoli Cui1101.01
Aiping Lu1200.68
Miao Jiang136312.07
Yaoxian Wang1402.70