Application of TPRMine Method for Identification of Temporal Changes on Patients with COPD: A Case Study in Telehealth

Inibhunu, C; McGregor, C

Application of TPRMine Method for Identification of Temporal Changes on Patients with COPD: A Case Study in Telehealth

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. 380-383
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	2021-01-09T20:55:04Z
dc.date.available	2021-01-09T20:55:04Z
dc.date.issued	2020-09-01
dc.identifier.citation	2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), 2020, 2020-July, pp. 380-383
dc.identifier.isbn	978-1-7281-9430-1
dc.identifier.issn	2372-918X
dc.identifier.uri	http://hdl.handle.net/10453/145232
dc.description.abstract	Monitoring the vital status of an aging population especially those with chronic diseases can potentially reduce the multiple emergency room visits and hospitalizations if patients and care providers are provided with information that might help them make informed decisions on appropriate cause of actions. This process can be enabled by utilizing temporal abstraction and deriving temporal patterns in order to understand the underlying temporal relationships on vital status in data collected from patients participating in telehealth programs in combination with other data sets in order to get a complete patient flow. Such discovery can highlight when an elderly patient is at risk of an adverse event and this is information that can be utilized for provision of appropriate care for the patient. This paper demonstrates application of a method for deriving temporal patterns from patient's physiological data thereby quantifying the many states a patient can transition to before, during and after an adverse event. With this approach, it is possible to quantify patients with vital scores based on their physiology.
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/cbms49503.2020.00078
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	Application of TPRMine Method for Identification of Temporal Changes on Patients with COPD: A Case Study in Telehealth
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	2021-01-09T20:54:46Z
pubs.finish-date	2020-07-30
pubs.publication-status	Published
pubs.start-date	2020-07-28
pubs.volume	2020-July

Abstract:

Monitoring the vital status of an aging population especially those with chronic diseases can potentially reduce the multiple emergency room visits and hospitalizations if patients and care providers are provided with information that might help them make informed decisions on appropriate cause of actions. This process can be enabled by utilizing temporal abstraction and deriving temporal patterns in order to understand the underlying temporal relationships on vital status in data collected from patients participating in telehealth programs in combination with other data sets in order to get a complete patient flow. Such discovery can highlight when an elderly patient is at risk of an adverse event and this is information that can be utilized for provision of appropriate care for the patient. This paper demonstrates application of a method for deriving temporal patterns from patient's physiological data thereby quantifying the many states a patient can transition to before, during and after an adverse event. With this approach, it is possible to quantify patients with vital scores based on their physiology.

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http://hdl.handle.net/10453/145232