Utilize este identificador para referenciar este registo: https://hdl.handle.net/1822/67830

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Campo DCValorIdioma
dc.contributor.authorPeixoto, Ricardopor
dc.contributor.authorRibeiro, Lisetepor
dc.contributor.authorPortela, Filipepor
dc.contributor.authorSantos, Manuelpor
dc.contributor.authorRua, Fernandopor
dc.date.accessioned2020-10-28T18:23:32Z-
dc.date.available2020-10-28T18:23:32Z-
dc.date.issued2017-
dc.identifier.issn1877-0509-
dc.identifier.urihttps://hdl.handle.net/1822/67830-
dc.description.abstractEvery day the surgical interventions are associated with medicine, and the area of critical care medicine is no exception. The goal of this work is to assist health professionals in predicting these interventions. Thus, when the Data Mining techniques are well applied it is possible, with the help of medical knowledge, to predict whether a particular patient should or not should be re-operated upon the same problem. In this study, some aspects, such as heart disease and age, and some data classes were built to improve the models created. In addition, several scenarios were created, with the objective can predict the resurgery patients. According the primary objective, the resurgery patients prediction, the metric used was the sensitivity, obtaining an approximate result of 90%.por
dc.description.sponsorshipThis work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013." This work is also supported by the Deus ex Machina (DEM): Symbiotic technology for societal efficiency gains - NORTE-01-0145-FEDER-000026por
dc.language.isoengpor
dc.publisherElsevier Science BVpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147280/PTpor
dc.rightsopenAccesspor
dc.subjectData Miningpor
dc.subjectClassificationpor
dc.subjectInterventionspor
dc.subjectReinterventionspor
dc.subjectINTCarepor
dc.titlePredicting resurgery in intensive care - a data mining approachpor
dc.typeconferencePaperpor
dc.peerreviewedyespor
dc.relation.publisherversionhttps://www.sciencedirect.com/science/article/pii/S1877050917317003por
oaire.citationStartPage577por
oaire.citationEndPage584por
oaire.citationVolume113por
dc.date.updated2020-10-28T12:00:42Z-
dc.identifier.doi10.1016/j.procs.2017.08.291por
dc.subject.fosCiências Naturais::Ciências da Computação e da Informaçãopor
dc.subject.fosEngenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informáticapor
dc.subject.wosScience & Technology-
sdum.export.identifier7408-
sdum.journalProcedia Computer Sciencepor
sdum.conferencePublication8TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS (EUSPN 2017) / 7TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE (ICTH-2017) / AFFILIATED WORKSHOPSpor
sdum.bookTitle8TH INTERNATIONAL CONFERENCE ON EMERGING UBIQUITOUS SYSTEMS AND PERVASIVE NETWORKS (EUSPN 2017) / 7TH INTERNATIONAL CONFERENCE ON CURRENT AND FUTURE TRENDS OF INFORMATION AND COMMUNICATION TECHNOLOGIES IN HEALTHCARE (ICTH-2017) / AFFILIATED WORKSHOPSpor
oaire.versionAMpor
Aparece nas coleções:CAlg - Artigos em livros de atas/Papers in proceedings

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