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Using text mining to diagnose and classify epilepsy in children

2013-10Comunicação em conferência Acesso restrito
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datacite.subject.fosCiências Médicas::Outras Ciências Médicas
datacite.subject.sdg08:Trabalho Digno e Crescimento Económico
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg10:Reduzir as Desigualdades
dc.contributor.authorLuis Pereira
dc.contributor.authorRijo, Rui
dc.contributor.authorSilva, Catarina
dc.contributor.authorAgostinho, Margarida
dc.date.accessioned2026-02-26T15:30:18Z
dc.date.available2026-02-26T15:30:18Z
dc.date.issued2013-10
dc.description.abstractEpilepsy diagnosis can be an extremely complex process, demanding considerable time and effort from physicians and healthcare infrastructures. Physicians need to classify each specific type of epilepsy based on different data, e.g., types of seizures, events and exams' results. This work presents a text mining approach to support medical decisions relating to epilepsy diagnosis and classification in children. We propose a text mining process that, using patient medical records, applies ontologies and named entities recognition as preprocessing steps, then applying K-Nearest Neighbors as a white-box lazy method to classify each instance. Results on real medical records suggest that the proposed framework shows good performance and clear interpretations, albeit the reduced volume of available training data.eng
dc.description.sponsorshipThe authors would like to thank to the Leiria-Pombal regional hospital (Hospital Santo André) that provided real anonymous patient records, which were crucial for this work. This work has been partially supported by the Portuguese Foundation for Science and Technology under project grant[s] PEst-C/ EEI/UI308/2011.
dc.identifier.citationL. Pereira, R. Rijo, C. Silva and M. Agostinho, "Using text mining to diagnose and classify epilepsy in children," 2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013), Lisbon, Portugal, 2013, pp. 345-349, doi: 10.1109/HealthCom.2013.6720698.
dc.identifier.doi10.1109/healthcom.2013.6720698
dc.identifier.isbn978-146735801-9
dc.identifier.urihttp://hdl.handle.net/10400.8/15726
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE Canada
dc.relation.hasversionhttps://ieeexplore.ieee.org/document/6720698
dc.relation.ispartof2013 IEEE 15th International Conference on e-Health Networking, Applications and Services (Healthcom 2013)
dc.rights.uriN/A
dc.subjectData mining
dc.subjectText mining
dc.subjectElectronic medical records
dc.subjectICD codes
dc.subjectMachine learning
dc.subjectEpilepsy
dc.titleUsing text mining to diagnose and classify epilepsy in childreneng
dc.typeconference paper
dspace.entity.typePublication
oaire.citation.conferenceDate2013
oaire.citation.conferencePlaceLisbon, Portugal
oaire.citation.endPage349
oaire.citation.startPage345
oaire.citation.title2013 IEEE 15th International Conference on e-Health Networking, Applications and Services, Healthcom 2013
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameRijo
person.familyNameSilva
person.givenNameRui Pedro Charters Lopes
person.givenNameCatarina
person.identifier.ciencia-idE71D-3237-849C
person.identifier.ciencia-id1B19-3DDC-BE75
person.identifier.orcid0000-0002-9348-0474
person.identifier.orcid0000-0002-5656-0061
person.identifier.scopus-author-id36861366200
relation.isAuthorOfPublicatione69d7599-392c-4f8f-a96a-bf0a0d15c8b1
relation.isAuthorOfPublicationee28e079-5ca7-4842-9094-372c40f75c38
relation.isAuthorOfPublication.latestForDiscoveryee28e079-5ca7-4842-9094-372c40f75c38

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