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acessibilidade

http://hdl.handle.net/10071/16609
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acessibilidade
DC FieldValueLanguage
dc.contributor.authorGonçalves, F.-
dc.contributor.authorPereira, R.-
dc.contributor.authorFerreira, J.-
dc.contributor.authorVasconcelos, J. B.-
dc.contributor.authorMelo, F.-
dc.contributor.authorVelez, I.-
dc.date.accessioned2018-09-14T09:47:00Z-
dc.date.available2018-09-14T09:47:00Z-
dc.date.issued2018-
dc.identifier.issn1819-9224-
dc.identifier.urihttp://hdl.handle.net/10071/16609-
dc.description.abstractThe Emergency Departments (ED) are a complex and important area of a hospital. With limited resources, it is mandatory to focus on efficiency. When hospitals are unable to deal with high demand, problems may rise leading to longer waiting times and more dissatisfaction. In this research, the authors extracted knowledge from a hospital ED, through data analysis and data mining, applying Random Forest and Naïve Bayes to study the ED patient waiting time and diseases.eng
dc.language.isoeng-
dc.publisherInternational Association of Engineers-
dc.relationUID/MULTI/4466/2016-
dc.relationUID/MULTI/0446/2013-
dc.rightsopenAccess-
dc.subjectBig dataeng
dc.subjectData miningeng
dc.subjectEmergency departmenteng
dc.subjectHealthcareeng
dc.titleEmergency waiting times data analysiseng
dc.typearticle-
dc.event.date2018-
dc.pagination494 - 499-
dc.peerreviewedyes-
dc.journalIAENG International Journal of Computer Science-
dc.volume45-
dc.number3-
degois.publication.firstPage494-
degois.publication.lastPage499-
degois.publication.issue3-
degois.publication.titleEmergency waiting times data analysiseng
dc.date.updated2018-09-14T10:46:44Z-
dc.description.versioninfo:eu-repo/semantics/publishedVersion-
dc.subject.fosDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informaçãopor
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-49750-
iscte.alternateIdentifiers.scopus2-s2.0-85052501691-
Appears in Collections:ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica

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