Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/31182

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dc.contributor.authorPortela, Filipepor
dc.contributor.authorSantos, Manuel Filipepor
dc.contributor.authorSilva, Álvaropor
dc.contributor.authorAbelha, Antóniopor
dc.contributor.authorMachado, José Manuelpor
dc.date.accessioned2014-11-24T12:31:08Z-
dc.date.available2014-11-24T12:31:08Z-
dc.date.issued2013-
dc.identifier.isbn978-3-642-54104-9-
dc.identifier.issn1865-0929por
dc.identifier.urihttp://hdl.handle.net/1822/31182-
dc.descriptionSeries : Communications in computer and information science, vol. 415por
dc.description.abstractThe number of patients admitted in Intensive care units (ICU) with organ failure is significant. This type of situation is common in Intensive Medicine. Intensive medicine is a specific area of medicine with the objective to avoid organ failure and recover patients in seriously ill conditions. This type of problems can culminate in the death of patient. In order to help the intensive medicine professionals in the moment of they take the decision, a Pervasive Intelligent Decision Support System called INTCare were developed. INTCare uses ensemble data mining to predict the probability of occurring an organ failure or patient die in the next hour. With the purpose to obtain always the better results, a measure was implemented to assess the models quality. All the transforming process and model induction are performed automatically and in real-time. The ensembles use online-learning to improve their models. In this paper the ensemble approach was explored and the results were compared at level of sensitivity, specificity, accuracy and total error. After the analysis was possible conclude that the ensembles are a too valid option to help the decision process in intensive Medicine.por
dc.description.sponsorship(undefined)por
dc.language.isoengpor
dc.publisherSpringer por
dc.rightsrestrictedAccesspor
dc.subjectData Miningpor
dc.subjectIntensive carepor
dc.subjectOrgan failurepor
dc.subjectPatient outcomepor
dc.subjectINTCarepor
dc.subjectEnsemblepor
dc.subjectReal-timepor
dc.subjectPervasive health carepor
dc.titlePervasive ensemble data mining models to predict organ failure and patient outcome in intensive medicinepor
dc.typeconferencePaperpor
dc.peerreviewedyespor
sdum.publicationstatuspublishedpor
oaire.citationStartPage410por
oaire.citationEndPage425por
oaire.citationConferencePlaceBarcelona, Spainpor
oaire.citationTitleKnowledge discovery, knowledge engineering and knowledge management : 4th International Joint Conferencepor
oaire.citationVolume415por
dc.identifier.doi10.1007/978-3-642-54105-6_27por
sdum.journalCommunications in Computer and Information Sciencepor
sdum.conferencePublicationKnowledge discovery, knowledge engineering and knowledge management : 4th International Joint Conferencepor
Appears in Collections:CCTC - Artigos em atas de conferências internacionais (texto completo)

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