Machine learning model for temporal pattern recognition

Inibhunu, C; McGregor, C

Machine learning model for temporal pattern recognition

Inibhunu, C McGregor, C

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2016 IEEE EMBS International Student Conference: Expanding the Boundaries of Biomedical Engineering and Healthcare, ISC 2016 - Proceedings, 2016, pp. 1-4
Issue Date:: 2016-07-08

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Field	Value	Language
dc.contributor.author	Inibhunu, C
dc.contributor.author	McGregor, C https://orcid.org/0000-0002-0491-4403
dc.date	2016-05-29
dc.date.accessioned	2023-02-02T20:20:28Z
dc.date.available	2023-02-02T20:20:28Z
dc.date.issued	2016-07-08
dc.identifier.citation	2016 IEEE EMBS International Student Conference: Expanding the Boundaries of Biomedical Engineering and Healthcare, ISC 2016 - Proceedings, 2016, pp. 1-4
dc.identifier.isbn	9781509009350
dc.identifier.uri	http://hdl.handle.net/10453/165859
dc.description.abstract	Temporal abstraction and data mining are two research fields that have tried to synthesis time oriented data and bring out an understanding on the hidden relationships that may exist between time oriented events. In clinical settings, having the ability to know the hidden relationships on patient data as they unfold could help save a life by aiding in detection of conditions that are not obvious to clinicians and healthcare workers. Understanding the hidden patterns is a huge challenge due to the exponential search space unique to time-series data. In this paper, we propose a temporal pattern recognition model based on dimension reduction and similarity measures thereby maintaining the temporal nature of the raw data.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2016 IEEE EMBS International Student Conference: Expanding the Boundaries of Biomedical Engineering and Healthcare, ISC 2016 - Proceedings
dc.relation.ispartof	2016 IEEE EMBS International Student Conference (ISC)
dc.relation.isbasedon	10.1109/EMBSISC.2016.7508614
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Machine learning model for temporal pattern recognition
dc.type	Conference Proceeding
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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-02-02T20:20:27Z
pubs.finish-date	2016-05-31
pubs.publication-status	Published
pubs.start-date	2016-05-29

Abstract:

Temporal abstraction and data mining are two research fields that have tried to synthesis time oriented data and bring out an understanding on the hidden relationships that may exist between time oriented events. In clinical settings, having the ability to know the hidden relationships on patient data as they unfold could help save a life by aiding in detection of conditions that are not obvious to clinicians and healthcare workers. Understanding the hidden patterns is a huge challenge due to the exponential search space unique to time-series data. In this paper, we propose a temporal pattern recognition model based on dimension reduction and similarity measures thereby maintaining the temporal nature of the raw data.

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http://hdl.handle.net/10453/165859