DocumentCode
2379896
Title
Application of Bayesian network in information fusion analysis of four diagnostic methods of traditional Chinese medicine
Author
Xu, Wenjie ; Wang, Yiqin ; Xu, Zhaoxia ; Chen, Chunfeng ; Zou, Xiaojuan
Author_Institution
Lab. of Four Diagnostic Inf. of TCM, Shanghai Univ. of TCM, Shanghai, China
fYear
2010
fDate
18-18 Dec. 2010
Firstpage
694
Lastpage
697
Abstract
Bayesian network is the effective tool for reasoning and modeling of complex and uncertain system based on traditional probability theory, which is widely used in uncertain decision-making, data analysis, intelligence reasoning and other fields. Syndrome differentiation and treatment, one of the basic characteristics of traditional Chinese medicine (TCM), is the essence of TCM. Fusion analysis on standardization and objectification of four diagnostic methods of TCM is the basic of syndrome differentiation analysis of TCM. The traditional methods are often with subjective differentiation and ambiguity, and the essence of syndrome differentiation of TCM can be seen as a classification problem. Bayesian network, as a better algorithm of data mining, is being increasingly applied to the study of syndrome differentiation of TCM. This article outlines the application of Bayesian network in information fusion analysis of four diagnostic methods of TCM and prospects for future research.
Keywords
belief networks; data mining; decision making; medical diagnostic computing; patient treatment; Bayesian network; TCM; data mining; decision making; information fusion analysis; intelligence reasoning; objectification; standardization; syndrome differentiation analysis; traditional Chinese medicine; Bayesian network; information fusion; traditional Chinese medical diagnosis;
fLanguage
English
Publisher
ieee
Conference_Titel
Bioinformatics and Biomedicine Workshops (BIBMW), 2010 IEEE International Conference on
Conference_Location
Hong, Kong
Print_ISBN
978-1-4244-8303-7
Electronic_ISBN
978-1-4244-8304-4
Type
conf
DOI
10.1109/BIBMW.2010.5703891
Filename
5703891
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