Extracting Disease-Phenotype Relations from Text with Disease-Phenotype Concept Recognisers and Association Rule Mining

Kocbek, S; Groza, T

Extracting Disease-Phenotype Relations from Text with Disease-Phenotype Concept Recognisers and Association Rule Mining

Kocbek, S Groza, T

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Publication Type:: Conference Proceeding
Citation:: Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2017, 2017-June pp. 358 - 363
Issue Date:: 2017-11-10

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Field	Value	Language
dc.contributor.author	Kocbek, S	en_US
dc.contributor.author	Groza, T	en_US
dc.date.issued	2017-11-10	en_US
dc.identifier.citation	Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2017, 2017-June pp. 358 - 363	en_US
dc.identifier.isbn	9781538617106	en_US
dc.identifier.issn	1063-7125	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127086
dc.description.abstract	© 2017 IEEE. Automatically extracting phenotypes (i.e., the composite of ones observable characteristics/traits) from free text such as scientific literature or clinical notes and associating phenotypes with diseases is an important task. Such associations can be used in, for example, recommending candidate genes for diseases, investigating drug targets, or performing differential diagnosis. In this paper, we focus on extracting disease-phenotype relations with association rule mining techniques and compare results with two other methods. We show that association rule mining offers promising alternative method for detecting disease-phenotype relations.	en_US
dc.relation.ispartof	Proceedings - IEEE Symposium on Computer-Based Medical Systems	en_US
dc.relation.isbasedon	10.1109/CBMS.2017.163	en_US
dc.title	Extracting Disease-Phenotype Relations from Text with Disease-Phenotype Concept Recognisers and Association Rule Mining	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2017-June	en_US
utslib.for	1004 Medical Biotechnology	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	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
pubs.volume	2017-June	en_US

Abstract:

© 2017 IEEE. Automatically extracting phenotypes (i.e., the composite of ones observable characteristics/traits) from free text such as scientific literature or clinical notes and associating phenotypes with diseases is an important task. Such associations can be used in, for example, recommending candidate genes for diseases, investigating drug targets, or performing differential diagnosis. In this paper, we focus on extracting disease-phenotype relations with association rule mining techniques and compare results with two other methods. We show that association rule mining offers promising alternative method for detecting disease-phenotype relations.

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http://hdl.handle.net/10453/127086