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dc.contributor.authorFernández-Breis, Jesualdo Tomás
dc.contributor.authorMaldonado, José A.
dc.contributor.authorMarcos, Mar
dc.contributor.authorLegaz-García, María del Carmen
dc.contributor.authorMoner, David
dc.contributor.authorTorres-Sospedra, Joaquín
dc.contributor.authorEsteban-Gil, Ángel
dc.contributor.authorMartínez Salvador, Begoña
dc.contributor.authorRobles, Montserrat
dc.date.accessioned2014-05-02T07:29:22Z
dc.date.available2014-05-02T07:29:22Z
dc.date.issued2013
dc.identifier.citationFERNÁNDEZ-BREIS, Jesualdo Tomás, et al. Leveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohorts. Journal of the American Medical Informatics Association, 2013, vol. 20, no e2, p. e288-e296.ca_CA
dc.identifier.issn1067-5027
dc.identifier.issn1527-974X
dc.identifier.urihttp://hdl.handle.net/10234/91153
dc.description.abstractBackground The secondary use of electronic healthcare records (EHRs) often requires the identification of patient cohorts. In this context, an important problem is the heterogeneity of clinical data sources, which can be overcome with the combined use of standardized information models, virtual health records, and semantic technologies, since each of them contributes to solving aspects related to the semantic interoperability of EHR data. Objective To develop methods allowing for a direct use of EHR data for the identification of patient cohorts leveraging current EHR standards and semantic web technologies. Materials and methods We propose to take advantage of the best features of working with EHR standards and ontologies. Our proposal is based on our previous results and experience working with both technological infrastructures. Our main principle is to perform each activity at the abstraction level with the most appropriate technology available. This means that part of the processing will be performed using archetypes (ie, data level) and the rest using ontologies (ie, knowledge level). Our approach will start working with EHR data in proprietary format, which will be first normalized and elaborated using EHR standards and then transformed into a semantic representation, which will be exploited by automated reasoning. Results We have applied our approach to protocols for colorectal cancer screening. The results comprise the archetypes, ontologies, and datasets developed for the standardization and semantic analysis of EHR data. Anonymized real data have been used and the patients have been successfully classified by the risk of developing colorectal cancer. Conclusions This work provides new insights in how archetypes and ontologies can be effectively combined for EHR-driven phenotyping. The methodological approach can be applied to other problems provided that suitable archetypes, ontologies, and classification rules can be designedca_CA
dc.format.extent9 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherAmerican Medical Informatics Associationca_CA
dc.relation.isPartOfJournal of the American Medical Informatics Association (2013) vol. 20, no e2ca_CA
dc.rightsCopyright © 2014 by the American Medical Informatics Association. All rights reserved.ca_CA
dc.subjectDecision Support Systemsca_CA
dc.subjectClinicalca_CA
dc.subjectElectronic Health Records/standardsca_CA
dc.subjectMedical Informaticsca_CA
dc.subjectSemanticsca_CA
dc.titleLeveraging electronic healthcare record standards and semantic web technologies for the identification of patient cohortsca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttp://dx.doi.org/10.1136/amiajnl-2013-00192
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttp://jamia.bmj.com/content/early/2013/08/09/amiajnl-2013-001923.abstract#aff-3ca_CA


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