Identification of Temporal Changes on Patients at Risk of LONS with TPRMine: A Case Study in NICU

Inibhunu, C; McGregor, C

Identification of Temporal Changes on Patients at Risk of LONS with TPRMine: A Case Study in NICU

Inibhunu, C McGregor, C

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020, 2020-July, pp. 33-36
Issue Date:: 2020-09-01

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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	2020-07-28
dc.date.accessioned	2021-01-09T20:53:46Z
dc.date.available	2021-01-09T20:53:46Z
dc.date.issued	2020-09-01
dc.identifier.citation	2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020, 2020-July, pp. 33-36
dc.identifier.isbn	978-1-7281-9430-1
dc.identifier.issn	2372-918X
dc.identifier.uri	http://hdl.handle.net/10453/145231
dc.description.abstract	A neonatal intensive care unit (NICU) provides specialized care for preterm or ill term infants. The onset of many conditions they can develop are not obvious to physicians until they are significantly impacted and this could result in death. An example of such a problem is neonatal infection which is a common cause of death for premature infants. It remains a challenging task for clinicians to accurately diagnose the presence of bacteria on patients with frequent presence of multiple comorbidities. There is potential for early detection of neonatal infections by timely analysis of patient physiological data and this can lead to improved health outcome of critically ill patients. This paper demonstrates application of a method for Temporal Pattern Recognition and Mining (TPRMine) in order to (a) understand if continuous analysis of temporal changes in patient physiological data streams can lead to discovery of pathophysiological patterns from patients at risk of neonatal sepsis and, (b) utilize the resulting analysis for formulating and testing hypothesis facilitating statistical quantification of patients.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS)
dc.relation.ispartof	2020 IEEE 33rd International Symposium on Computer-Based Medical Systems
dc.relation.isbasedon	10.1109/cbms49503.2020.00014
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Identification of Temporal Changes on Patients at Risk of LONS with TPRMine: A Case Study in NICU
dc.type	Conference Proceeding
utslib.citation.volume	2020-July
utslib.location.activity	Rochester, MN, USA
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
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-09-01T00:00:00+1000Z
dc.date.updated	2021-01-09T20:53:28Z
pubs.finish-date	2020-07-30
pubs.publication-status	Published
pubs.start-date	2020-07-28
pubs.volume	2020-July

Abstract:

A neonatal intensive care unit (NICU) provides specialized care for preterm or ill term infants. The onset of many conditions they can develop are not obvious to physicians until they are significantly impacted and this could result in death. An example of such a problem is neonatal infection which is a common cause of death for premature infants. It remains a challenging task for clinicians to accurately diagnose the presence of bacteria on patients with frequent presence of multiple comorbidities. There is potential for early detection of neonatal infections by timely analysis of patient physiological data and this can lead to improved health outcome of critically ill patients. This paper demonstrates application of a method for Temporal Pattern Recognition and Mining (TPRMine) in order to (a) understand if continuous analysis of temporal changes in patient physiological data streams can lead to discovery of pathophysiological patterns from patients at risk of neonatal sepsis and, (b) utilize the resulting analysis for formulating and testing hypothesis facilitating statistical quantification of patients.

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http://hdl.handle.net/10453/145231