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Named Entity Recognition in Electronic Health Records: A Methodological Review

dc.contributor.authorDurango M.C
dc.contributor.authorTorres-Silva E.A
dc.contributor.authorOrozco-Duque A.
dc.date.accessioned2024-07-31T21:07:20Z
dc.date.available2024-07-31T21:07:20Z
dc.date.created2023
dc.identifier.issn20933681
dc.identifier.urihttp://hdl.handle.net/11407/8544
dc.descriptionObjectives: A substantial portion of the data contained in Electronic Health Records (EHR) is unstructured, often appearing as free text. This format restricts its potential utility in clinical decision-making. Named entity recognition (NER) methods address the challenge of extracting pertinent information from unstructured text. The aim of this study was to outline the current NER methods and trace their evolution from 2011 to 2022. Methods: We conducted a methodological literature review of NER methods, with a focus on distinguishing the classification models, the types of tagging systems, and the languages employed in various corpora. Results: Several methods have been documented for automatically extracting relevant information from EHRs using natural language processing techniques such as NER and relation extraction (RE). These methods can automatically extract concepts, events, attributes, and other data, as well as the relationships between them. Most NER studies conducted thus far have utilized corpora in english or Chinese. Additionally, the bidirectional encoder representation from transformers using the BIO tagging system architecture is the most frequently reported classification scheme. We discovered a limited number of papers on the implementation of NER or RE tasks in EHRs within a specific clinical domain. Conclusions: EHRs play a pivotal role in gathering clinical information and could serve as the primary source for automated clinical decision support systems. However, the creation of new corpora from EHRs in specific clinical domains is essential to facilitate the swift development of NER and RE models applied to EHRs for use in clinical practice. © 2023 The Korean Society of Medical Informatics.
dc.language.isoeng
dc.publisherKorean Society of Medical Informatics
dc.relation.isversionofhttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85176382158&doi=10.4258%2fhir.2023.29.4.286&partnerID=40&md5=b7f884d63c6feef29fec9c5d99bf6d26
dc.sourceHealthcare Informatics Research
dc.sourceHealthc. Informatics Res.
dc.sourceScopus
dc.subjectClinical Decision Support Systemeng
dc.subjectDeep Learningeng
dc.subjectElectronic Health Recordseng
dc.subjectNatural Language Processingeng
dc.subjectSupervised Machine Learningeng
dc.titleNamed Entity Recognition in Electronic Health Records: A Methodological Revieweng
dc.typereview
dc.rights.accessrightsinfo:eu-repo/semantics/restrictedAccess
dc.publisher.programIngeniería de Telecomunicacionesspa
dc.type.spaRevisión
dc.identifier.doi10.4258/hir.2023.29.4.286
dc.relation.citationvolume29
dc.relation.citationissue4
dc.relation.citationstartpage286
dc.relation.citationendpage300
dc.publisher.facultyFacultad de Ingenieríasspa
dc.affiliationDurango, M.C., Grupo de Investigación e Innovación Biomédica, Instituto Tecnológico Metropolitano, Antioquia, Colombia
dc.affiliationTorres-Silva, E.A., Grupo de Investigación e Innovación Biomédica, Instituto Tecnológico Metropolitano, Antioquia, Colombia
dc.affiliationOrozco-Duque, A., Grupo de Investigación e Innovación Biomédica, Instituto Tecnológico Metropolitano, Antioquia, Colombia, Facultad de Ingenierías, Universidad de Medellín, Antioquia, Colombia
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dc.type.versioninfo:eu-repo/semantics/publishedVersion
dc.identifier.reponamereponame:Repositorio Institucional Universidad de Medellín
dc.identifier.repourlrepourl:https://repository.udem.edu.co/
dc.identifier.instnameinstname:Universidad de Medellín


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