Stratifying Risk of Coronary Artery Disease Using Discriminative Knowledge-Guided Medical Concept Pairings from Clinical Notes

Abdollahi, M; Gao, X; Mei, Y; Ghosh, S; Li, J

Stratifying Risk of Coronary Artery Disease Using Discriminative Knowledge-Guided Medical Concept Pairings from Clinical Notes

Abdollahi, M Gao, X Mei, Y Ghosh, S Li, J

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Publisher:: Springer International Publishing
Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11672 LNAI, pp. 457-473
Issue Date:: 2019-01-01

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Field	Value	Language
dc.contributor.author	Abdollahi, M
dc.contributor.author	Gao, X
dc.contributor.author	Mei, Y
dc.contributor.author	Ghosh, S
dc.contributor.author	Li, J https://orcid.org/0000-0003-1833-7413
dc.date.accessioned	2020-06-17T22:29:20Z
dc.date.available	2020-06-17T22:29:20Z
dc.date.issued	2019-01-01
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2019, 11672 LNAI, pp. 457-473
dc.identifier.isbn	9783030298937
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/141501
dc.description.abstract	© 2019, Springer Nature Switzerland AG. Document classification (DC) is one of the broadly investigated natural language processing tasks. Medical document classification can support doctors in making decision and improve medical services. Since the data in document classification often appear in raw form such as medical discharge notes, extracting meaningful information to use as features is a challenging task. There are many specialized words and expressions in medical documents which make them more challenging to analyze. The classification accuracy of available methods in medical field is not good enough. This work aims to improve the quality of the input feature sets to increase the accuracy. A new three-stage approach is proposed. In the first stage, the Unified Medical Language System (UMLS) which is a medical-specific dictionary is used to extract the meaningful phrases by considering disease or symptom concepts. In the second stage, all the possible pairs of the extracted concepts are created as new features. In the third stage, Particle Swarm Optimisation (PSO) is employed to select features from the extracted and constructed features in the previous stages. The experimental results show that the proposed three-stage method achieved substantial improvement over the existing medical DC approaches.
dc.language	en
dc.publisher	Springer International Publishing
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-29894-4_37
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Stratifying Risk of Coronary Artery Disease Using Discriminative Knowledge-Guided Medical Concept Pairings from Clinical Notes
dc.type	Conference Proceeding
utslib.citation.volume	11672 LNAI
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Strength - AAI - Advanced Analytics Institute Research Centre
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
dc.date.updated	2020-06-17T22:29:10Z
pubs.publication-status	Published
pubs.volume	11672 LNAI

Abstract:

© 2019, Springer Nature Switzerland AG. Document classification (DC) is one of the broadly investigated natural language processing tasks. Medical document classification can support doctors in making decision and improve medical services. Since the data in document classification often appear in raw form such as medical discharge notes, extracting meaningful information to use as features is a challenging task. There are many specialized words and expressions in medical documents which make them more challenging to analyze. The classification accuracy of available methods in medical field is not good enough. This work aims to improve the quality of the input feature sets to increase the accuracy. A new three-stage approach is proposed. In the first stage, the Unified Medical Language System (UMLS) which is a medical-specific dictionary is used to extract the meaningful phrases by considering disease or symptom concepts. In the second stage, all the possible pairs of the extracted concepts are created as new features. In the third stage, Particle Swarm Optimisation (PSO) is employed to select features from the extracted and constructed features in the previous stages. The experimental results show that the proposed three-stage method achieved substantial improvement over the existing medical DC approaches.

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http://hdl.handle.net/10453/141501