StrokePEO: Construction of a Clinical Ontology for Physical Examination of Stroke

Gu, Z; Yang, X; Jia, W; Xu, C; Yu, P; He, X; Chen, H; Lin, Y

StrokePEO: Construction of a Clinical Ontology for Physical Examination of Stroke

Gu, Z Yang, X Jia, W

Xu, C Yu, P He, X Chen, H Lin, Y

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: Proceedings - 2022 9th International Conference on Digital Home, ICDH 2022, 2022, 00, pp. 218-223
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Gu, Z
dc.contributor.author	Yang, X
dc.contributor.author	Jia, W https://orcid.org/0000-0002-0940-3338
dc.contributor.author	Xu, C
dc.contributor.author	Yu, P
dc.contributor.author	He, X
dc.contributor.author	Chen, H
dc.contributor.author	Lin, Y
dc.date	2022-10-28
dc.date.accessioned	2023-03-20T14:48:35Z
dc.date.available	2023-03-20T14:48:35Z
dc.date.issued	2022-01-01
dc.identifier.citation	Proceedings - 2022 9th International Conference on Digital Home, ICDH 2022, 2022, 00, pp. 218-223
dc.identifier.isbn	9781665454780
dc.identifier.uri	http://hdl.handle.net/10453/167784
dc.description.abstract	Clinical ontology is a standardized medical knowledge representation model that facilitates the integration and analysis of a large amount of heterogeneous electronic health record (EHR) data. Using ontologies to represent clinical terms can improve data integration to build robust and interoperable medical information systems. To date, there is no ontology existing to represent the medical knowledge for physical examination of stroke, which has inhibited the stroke physicians to make full use of clinical information captured in EHR data to understand stroke patient's health status and plan effective medication and rehabilitation treatment. In this research, we co-design with two stroke clinical specialists a stroke clinical ontology "StrokePEO"using advanced natural language processing and deep learning techniques to extract terms and their relationships from real clinical case records provided by a tertiary hospital in China. We apply the W3C Resource Description Framework (RDF) data model to represent these clinical terms and relationships, and successfully store all case data in a graph database with StrokePEO. Our experiment results suggest that our methods and the output of StrokePEO can be applied in various medical contexts that require extraction of medical knowledge from free text for decision making. These include, but not limited to, physical assessment, drug and rehabilitation treatment outcome evaluation, medication effect analysis, and patient risk prediction.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	Proceedings - 2022 9th International Conference on Digital Home, ICDH 2022
dc.relation.ispartof	2022 9th International Conference on Digital Home (ICDH)
dc.relation.isbasedon	10.1109/ICDH57206.2022.00041
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	StrokePEO: Construction of a Clinical Ontology for Physical Examination of Stroke
dc.type	Conference Proceeding
utslib.citation.volume	00
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 - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2024-01-01T00:00:00+1000Z
dc.date.updated	2023-03-20T14:48:33Z
pubs.finish-date	2022-10-30
pubs.publication-status	Published
pubs.start-date	2022-10-28
pubs.volume	00

Abstract:

Clinical ontology is a standardized medical knowledge representation model that facilitates the integration and analysis of a large amount of heterogeneous electronic health record (EHR) data. Using ontologies to represent clinical terms can improve data integration to build robust and interoperable medical information systems. To date, there is no ontology existing to represent the medical knowledge for physical examination of stroke, which has inhibited the stroke physicians to make full use of clinical information captured in EHR data to understand stroke patient's health status and plan effective medication and rehabilitation treatment. In this research, we co-design with two stroke clinical specialists a stroke clinical ontology "StrokePEO"using advanced natural language processing and deep learning techniques to extract terms and their relationships from real clinical case records provided by a tertiary hospital in China. We apply the W3C Resource Description Framework (RDF) data model to represent these clinical terms and relationships, and successfully store all case data in a graph database with StrokePEO. Our experiment results suggest that our methods and the output of StrokePEO can be applied in various medical contexts that require extraction of medical knowledge from free text for decision making. These include, but not limited to, physical assessment, drug and rehabilitation treatment outcome evaluation, medication effect analysis, and patient risk prediction.

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http://hdl.handle.net/10453/167784