PDD graph: Bridging electronic medical records and biomedical knowledge graphs via entity linking

Wang, M; Zhang, J; Liu, J; Hu, W; Wang, S; Li, X; Liu, W

PDD graph: Bridging electronic medical records and biomedical knowledge graphs via entity linking

Wang, M Zhang, J Liu, J Hu, W Wang, S Li, X Liu, W

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Publication Type:: Conference Proceeding
Citation:: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10588 LNCS pp. 219 - 227
Issue Date:: 2017-01-01

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Field	Value	Language
dc.contributor.author	Wang, M	en_US
dc.contributor.author	Zhang, J	en_US
dc.contributor.author	Liu, J	en_US
dc.contributor.author	Hu, W	en_US
dc.contributor.author	Wang, S	en_US
dc.contributor.author	Li, X	en_US
dc.contributor.author	Liu, W	en_US
dc.date.issued	2017-01-01	en_US
dc.identifier.citation	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2017, 10588 LNCS pp. 219 - 227	en_US
dc.identifier.isbn	9783319682037	en_US
dc.identifier.issn	0302-9743	en_US
dc.identifier.uri	http://hdl.handle.net/10453/127546
dc.description.abstract	© Springer International Publishing AG 2017. Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient’s symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis, and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogeneous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.	en_US
dc.relation.ispartof	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)	en_US
dc.relation.isbasedon	10.1007/978-3-319-68204-4_23	en_US
dc.subject.classification	Artificial Intelligence & Image Processing	en_US
dc.title	PDD graph: Bridging electronic medical records and biomedical knowledge graphs via entity linking	en_US
dc.type	Conference Proceeding
utslib.citation.volume	10588 LNCS	en_US
utslib.for	1117 Public Health and Health Services	en_US
utslib.for	0806 Information Systems	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.publication-status	Published	en_US
pubs.volume	10588 LNCS	en_US

Abstract:

© Springer International Publishing AG 2017. Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patient’s symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis, and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogeneous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.

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http://hdl.handle.net/10453/127546