An approach of hierarchical concept clustering on Medical Short Text corpus

Li, W; Zhao, D; Yang, J; Cao, L

An approach of hierarchical concept clustering on Medical Short Text corpus

Li, W Zhao, D Yang, J Cao, L

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the 2013 6th International Conference on Biomedical Engineering and Informatics, BMEI 2013, 2013, pp. 509 - 518
Issue Date:: 2013-01-01

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Field	Value	Language
dc.contributor.author	Li, W	en_US
dc.contributor.author	Zhao, D	en_US
dc.contributor.author	Yang, J	en_US
dc.contributor.author	Cao, L https://orcid.org/0000-0003-1562-9429	en_US
dc.date.issued	2013-01-01	en_US
dc.identifier.citation	Proceedings of the 2013 6th International Conference on Biomedical Engineering and Informatics, BMEI 2013, 2013, pp. 509 - 518	en_US
dc.identifier.uri	http://hdl.handle.net/10453/28427
dc.description.abstract	Hierarchical clustering and conceptual clustering are two important types of clustering analysis methods. A variety of approaches have been proposed in previous works. However, seldom methods are designed to run on the medical short text database and construct a hierarchical concept taxonomy. This paper proposes a new clustering method of Hierarchical Concept Clustering on Medical Short Text corpus (HCCST), which presents a new solution on actionable disease taxonomy construction from the actual medical data. Our approach has three advantages. Firstly, HCCST takes a new similarity method which covers all the problems in medical short text distance computing. Secondly, an adaptive clustering method is proposed for synonymous disease names without predefining the size of clusters. Thirdly, this paper uses a mutual information based potential hierarchy concept pair recognition method which improves the subsumption method to create hierarchical disease taxonomy. The evaluation is conducted on Chinese medical disease name text data set and the result shows that HCCST achieves satisfactory performance. © 2013 IEEE.	en_US
dc.relation.ispartof	Proceedings of the 2013 6th International Conference on Biomedical Engineering and Informatics, BMEI 2013	en_US
dc.relation.isbasedon	10.1109/BMEI.2013.6746995	en_US
dc.title	An approach of hierarchical concept clustering on Medical Short Text corpus	en_US
dc.type	Conference Proceeding
utslib.for	0903 Biomedical Engineering	en_US
dc.location.activity	Hangzhou, China	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US

Abstract:

Hierarchical clustering and conceptual clustering are two important types of clustering analysis methods. A variety of approaches have been proposed in previous works. However, seldom methods are designed to run on the medical short text database and construct a hierarchical concept taxonomy. This paper proposes a new clustering method of Hierarchical Concept Clustering on Medical Short Text corpus (HCCST), which presents a new solution on actionable disease taxonomy construction from the actual medical data. Our approach has three advantages. Firstly, HCCST takes a new similarity method which covers all the problems in medical short text distance computing. Secondly, an adaptive clustering method is proposed for synonymous disease names without predefining the size of clusters. Thirdly, this paper uses a mutual information based potential hierarchy concept pair recognition method which improves the subsumption method to create hierarchical disease taxonomy. The evaluation is conducted on Chinese medical disease name text data set and the result shows that HCCST achieves satisfactory performance. © 2013 IEEE.

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http://hdl.handle.net/10453/28427