Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/51692

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dc.contributor.authorFonseca, Franciscapor
dc.contributor.authorPeixoto, Hugo Daniel Abreupor
dc.contributor.authorMiranda, Filipe Manuel Motapor
dc.contributor.authorMachado, José Manuelpor
dc.contributor.authorAbelha, Antóniopor
dc.date.accessioned2018-03-07T11:39:52Z-
dc.date.issued2017-
dc.identifier.issn1877-0509-
dc.identifier.urihttps://hdl.handle.net/1822/51692-
dc.description"International Workshop on Healthcare Interoperability and Pervasive Intelligent Systems (HiPIS 2017)"por
dc.description.abstractThe aim of this study is to predict, through data mining tools, the incidence of perineal tear. This kind of laceration developed during child delivery might imply surgery and entails a set of several consequences. Clinical Decision Support Systems, with the information collected from patients' electronic health records combined with the data mining techniques, may decrease the incidence of perineal tears during labour.por
dc.description.sponsorshipThis work has been supported by Compete: POCI-01-0145-FEDER-007043 and FCT within the Project Scope UID/CEC/00319/2013.por
dc.language.isoengpor
dc.publisherElsevier 1por
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147280/PTpor
dc.rightsopenAccesspor
dc.subjectData Miningpor
dc.subjectObstetricspor
dc.subjectPerineal Tearpor
dc.subjectDecision Support Systemspor
dc.titleStep towards prediction of perineal tearpor
dc.typeconferencePaperpor
dc.peerreviewedyespor
oaire.citationStartPage565por
oaire.citationEndPage570por
oaire.citationVolume113por
dc.date.updated2018-02-17T14:35:28Z-
dc.identifier.doi10.1016/j.procs.2017.08.284por
dc.description.publicationversioninfo:eu-repo/semantics/publishedVersionpor
sdum.export.identifier2775-
sdum.journalProcedia Computer Sciencepor
sdum.conferencePublicationProcedia Computer Sciencepor
Aparece nas coleções:CAlg - Artigos em livros de atas/Papers in proceedings

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