Semantic Description of Health Record Data for Procedural Interoperability

Vejvalka, Jan; Lesný, Petr; Holeček, Tomáš; Slabý, Kryštof; Krásničanová, Hana; Jarolímková, Adéla; Bouzková, Helena

doi:10.1007/978-3-642-11745-9_20

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2014

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“…This objective does not require nominative patient data even if unambiguity on patient identification and data ownership is necessary to correlate data entries. Data interoperability can be greatly enhanced by using a semantic approach to define, describe information to exchange and facilitate the exploitation of data available in multiple data sources [9], [10]. Therefore, the integration of dedicated technologies to deal with a semantic description of medical data is needed to aggregate additional information about patients' follow-up and highlight variables of interest regarding epidemiology.…”

Section: Introductionmentioning

confidence: 99%

Global Initiative for Sentinel e-Health Network on Grid (GINSENG): Medical Data Integration and Semantic Developments for Epidemiology

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Erétéo²,

Gaignard

et al. 2014

2014 14th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing

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International audienceThe implementation of a grid network to support large-scale epidemiology analysis (based on distributed medical data sources) and medical data sharing require medical data integration and semantic alignment. In this paper, we present the GINSENG (Global Initiative for Sentinel e-Health Network on Grid) network that federates existing Electronic Health Records through a rich metamodel (FedEHR), a semantic data model (SemEHR) and distributed query toolkits. A query interface based on the VIP platform, and available through the e-ginseng.org web portal helps medical end-users in the design of epidemiological studies and the retrieval of relevant medical data sets

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Semantic Description of Health Record Data for Procedural Interoperability

Cited by 1 publication

References 2 publications

Global Initiative for Sentinel e-Health Network on Grid (GINSENG): Medical Data Integration and Semantic Developments for Epidemiology

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