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
Link To Document :
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