Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/23938

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dc.contributor.authorGonçalves, João-
dc.contributor.authorPortela, Filipe-
dc.contributor.authorSantos, Manuel Filipe-
dc.contributor.authorSilva, Álvaro-
dc.contributor.authorMachado, José Manuel-
dc.contributor.authorAbelha, António-
dc.date.accessioned2013-05-06T10:16:31Z-
dc.date.available2013-05-06T10:16:31Z-
dc.date.issued2013-04-
dc.identifier.isbn978-3-642-36981-0-
dc.identifier.issn2194-5357por
dc.identifier.urihttp://hdl.handle.net/1822/23938-
dc.description.abstractThis paper aims to support doctor’s decision-making on predicting the Sepsis level. Thus, a set of Data Mining (DM) models were developed using prevision techniques and classification models. These models enable a better doctor’s decision having into account the Sepsis level of the patient. The DM models use real data collected from the Intensive Care Unit of the Santo António Hospital, in Oporto, Portugal. Classification DM models were considered to predict sepsis level in a supervised learning approach. The models were induced making use of the following algorithms: Decision Trees, Support Vector Machines and Naïve Bayes classifier. The models were assessed using the Confusion Matrix, associated metrics, and Cross-validation. The analysis of the total error rate, sensitivity, specificity and accuracy were the metrics used to identify the most relevant measures to predict sepsis level. This work demonstrates that it is possible to predict with great accuracy the sepsis level.por
dc.language.isoengpor
dc.publisherSpringer por
dc.rightsrestrictedAccesspor
dc.subjectData mIningpor
dc.subjectClassificationpor
dc.subjectIntensive carepor
dc.subjectSepsispor
dc.subjectINTCarepor
dc.titlePredict sepsis level in intensive medicine : data mining approachpor
dc.typeconferencePaperpor
sdum.publicationstatuspublishedpor
oaire.citationStartPage201por
oaire.citationEndPage211por
oaire.citationTitleAdvances in Information Systems and Technologies, Advances in Intelligent Systems and Computingpor
oaire.citationVolume206por
dc.identifier.doi10.1007/978-3-642-36981-0_19por
dc.subject.wosScience & Technologypor
sdum.journalAdvances in Intelligent Systems and Computingpor
sdum.conferencePublicationAdvances in Information Systems and Technologies, Advances in Intelligent Systems and Computingpor
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals
CCTC - Artigos em atas de conferências internacionais (texto completo)

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