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Decision Support System to Diagnosis and Classification of Epilepsy in Children

2014-06-01Artigo científico Acesso aberto
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.fosCiências Médicas::Ciências da Saúde
datacite.subject.sdg03:Saúde de Qualidade
dc.contributor.authorRijo, Rui
dc.contributor.authorSilva, Catarina
dc.contributor.authorPereira, Luis
dc.contributor.authorGonçalves, Dulce
dc.contributor.authorAgostinho, Margarida
dc.date.accessioned2026-05-07T13:50:53Z
dc.date.available2026-05-07T13:50:53Z
dc.date.issued2014-06-01
dc.descriptionArticle number - 23254.
dc.description.abstractClinical decision support systems play an important role in organizations. They have a tight relation with the information systems. Our goal is to develop a system to support the diagnosis and the classification of epilepsy in children. Around 50 million people in the world have epilepsy. Epilepsy diagnosis can be an extremely complex process, demanding considerable time and effort from physicians and healthcare infrastructures. Exams such as electroencephalograms and magnetic resonances are often used to create a more accurate diagnosis in a short amount of time. After the diagnosis process, physicians classify epilepsy according to the International Classification of Diseases, ninth revision (ICD-9). Physicians need to classify each specific type of epilepsy based on different data, e.g., types of seizures, events and exams' results. The classification process is time consuming and, in some cases, demands for complementary exams. This work presents a text mining approach to support medical decisions relating to epilepsy diagnosis and ICD-9-based classification in children. We put forward a text mining approach using electronically processed medical records, and apply the K-Nearest Neighbor technique as a white-box multiclass classifier approach to classify each instance, mapping it to the corresponding ICD-9-based standard code. Results on real medical records suggest that the proposed framework shows good performance and clear interpretations, albeit the reduced volume of available training data. To overcome this hurdle, in this work we also propose and explore ways of expanding the dataset.eng
dc.description.sponsorshipThe authors would like to thank to the pediatric service of Centro Hospitalar Leiria Pombal, namely to Hospital Santo André, which provided real anonymous patient records and participated with a clinical team in this research. 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.citationRijo, R., Silva, C., Pereira, L., Gonçalves, D., & Agostinho, M. (2014). Decision Support System to Diagnosis and Classification of Epilepsy in Children. Journal of Universal Computer Science, 20(6), 907-923. https://doi.org/10.3217/jucs-020-06-0907
dc.identifier.doi10.3217/jucs-020-06-0907
dc.identifier.issn0948-695x
dc.identifier.urihttp://hdl.handle.net/10400.8/16249
dc.language.isoeng
dc.peerreviewedyes
dc.publisherJ.UCS
dc.relation.hasversionhttps://lib.jucs.org/article/23254/
dc.rights.uriN/A
dc.subjectDecision Support System
dc.subjectEpilepsy
dc.subjectText Mining
dc.subjectClassification
dc.subjectDiagnosis
dc.subjectICD-9
dc.titleDecision Support System to Diagnosis and Classification of Epilepsy in Childreneng
dc.typejournal article
dspace.entity.typePublication
oaire.citation.endPage923
oaire.citation.issue6
oaire.citation.startPage907
oaire.citation.titleJournal of Universal Computer Science
oaire.citation.volume20
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNameRijo
person.familyNameSilva
person.familyNamePereira
person.familyNameGonçalves
person.givenNameRui Pedro Charters Lopes
person.givenNameCatarina
person.givenNameLuis
person.givenNameDulce
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.orcid0000-0002-1447-0471
person.identifier.orcid0000-0001-5555-6848
person.identifier.scopus-author-id36861366200
relation.isAuthorOfPublicatione69d7599-392c-4f8f-a96a-bf0a0d15c8b1
relation.isAuthorOfPublicationee28e079-5ca7-4842-9094-372c40f75c38
relation.isAuthorOfPublication8d8f2d91-46c7-48b5-9c93-b30b96a8b43c
relation.isAuthorOfPublicationc0251d02-2b1c-4485-a217-e0f3b05bcf36
relation.isAuthorOfPublication.latestForDiscoverye69d7599-392c-4f8f-a96a-bf0a0d15c8b1

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