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Concept attribute labeling and context-aware named entity recognition in electronic health records

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dc.contributor.authorPomares-Quimbaya, Alexandra
dc.contributor.authorGonzález, Rafael A.
dc.contributor.authorMuñoz, Óscar
dc.contributor.authorGarcia-Pena, A.A.
dc.contributor.authorDaza Rodríguez, Julián Camilo
dc.contributor.authorSierra Múnera, Alejandro
dc.contributor.authorLabbé, Cyril
dc.contributor.corporatenamePontificia Universidad Javeriana. Facultad de Medicina. Departamento de Medicina Interna. Cardiología
dc.contributor.corporatenamePontificia Universidad Javeriana. Facultad de Medicina. Departamento de Medicina Interna. Medicina Interna
dc.contributor.javerianateacherGarcia-Pena, A.A.
dc.contributor.javerianateacherMuñoz, Óscar
dc.date.accessioned2021-09-13T13:47:20Z
dc.date.available2021-09-13T13:47:20Z
dc.date.created2020
dc.description.abstractenglishExtracting valuable knowledge from Electronic Health Records (EHR) represents a challenging task due to the presence of both structured and unstructured data, including codified fields, images and test results. Narrative text in particular contains a variety of notes which are diverse in language and detail, as well as being full of ad hoc terminology, including acronyms and jargon, which is especially challenging in non-English EHR, where there is a dearth of annotated corpora or trained case sets. This paper proposes an approach for NER and concept attribute labeling for EHR that takes into consideration the contextual words around the entity of interest to determine its sense. The approach proposes a composition method of three different NER methods, together with the analysis of the context (neighboring words) using an ensemble classification model. This contributes to disambiguate NER, as well as labeling the concept as confirmed, negated, speculative, pending or antecedent. Results show an improvement of the recall and a limited impact on precision for the NER process.spa
dc.description.orcidhttps://orcid.org/0000-0002-3606-2102
dc.description.orcidhttps://orcid.org/0000-0001-5401-0018
dc.formatPDFspa
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttp://dx.doi.org/10.4018/978-1-7998-1204-3.ch017spa
dc.identifier.instnameinstname:Pontificia Universidad Javerianaspa
dc.identifier.isbn9781799812043 / 9781799812050 (Electrónico)spa
dc.identifier.reponamereponame:Repositorio Institucional - Pontificia Universidad Javerianaspa
dc.identifier.repourlrepourl:https://repository.javeriana.edu.cospa
dc.identifier.urihttp://hdl.handle.net/10554/57112
dc.language.isoN/Aspa
dc.publisherIGI Globalspa
dc.relation.ispartofbookData Analytics in Medicine: Concepts, Methodologies, Tools, and Applicationsspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa
dc.rights.licenceAtribución-NoComercial 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc/4.0/*
dc.titleConcept attribute labeling and context-aware named entity recognition in electronic health recordsspa
dc.type.coarhttp://purl.org/coar/resource_type/c_3248
dc.type.hasversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.localCapítulo de librospa

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