DocumentCode :
2123615
Title :
Network Analysis System for Traditional Chinese Medicine Clinical Data
Author :
Zhou, Xuezhong ; Liu, Baoyan
Author_Institution :
Sch. of Comput. Sci. & Inf. Technol., Beijing Jiaotong Univ., Beijing, China
fYear :
2009
fDate :
17-19 Oct. 2009
Firstpage :
1
Lastpage :
5
Abstract :
Traditional Chinese medicine (TCM) is a clinical medical discipline with clinical data as one of the main knowledge sources. The clinical information (e.g. symptoms, diagnoses and herb prescriptions) that is captured, generated and used by TCM physicians has complicated inter or intra relationships between the different elements. Due to the cooccurrence and combinational properties, TCM clinical data could naturally be represented by networks. This paper introduces a complex network analysis system to model and analyze the TCM clinical data. The system could automatically generate the networks from the clinical database, query the generated network data from the database and has the social network analysis abilities (e.g. measurements and community identification). It integrated the TCM knowledge (e.g. herb properties) to visualize the clinical data (e.g. herb prescriptions, symptoms and diagnoses) by networks, and can help acquire the core medical structures or relationships from the large-scale clinical data. It shows that the system provides a helpful platform for TCM clinical data analysis and the network analyses could generate clinically meaningful knowledge.
Keywords :
data analysis; data mining; medical computing; clinical data mining; clinical database; knowledge sources; network analysis system; traditional Chinese medicine clinical data analysis; Biological systems; Cells (biology); Complex networks; Data analysis; Data mining; Data warehouses; Diseases; Medical diagnostic imaging; Medical treatment; Visual databases;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Biomedical Engineering and Informatics, 2009. BMEI '09. 2nd International Conference on
Conference_Location :
Tianjin
Print_ISBN :
978-1-4244-4132-7
Electronic_ISBN :
978-1-4244-4134-1
Type :
conf
DOI :
10.1109/BMEI.2009.5302924
Filename :
5302924
Link To Document :
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