Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/58302

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dc.contributor.authorOliveira, Sérgio Manuel Costapor
dc.contributor.authorPortela, Filipepor
dc.contributor.authorSantos, Manuelpor
dc.contributor.authorMachado, José Manuelpor
dc.contributor.authorAbelha, Antóniopor
dc.contributor.authorSilva, Álvaropor
dc.contributor.authorRua, Fernandopor
dc.date.accessioned2019-01-16T16:20:38Z-
dc.date.issued2016-
dc.identifier.urihttp://hdl.handle.net/1822/58302-
dc.description.abstractThe weaning process of ventilated patients need to be carefully performed. This type of procedure is very common in Intensive Medicine. The procedure is well-defined and it is executed according the patient condition. During the weaning process, the patient can be in vary stages. At the end the extubation tentative can be considered as successful or not. Before the extubation, the patient is submitted to a set of tests in order to validate the procedure. When this procedure is wrong executed, it can provoke long term injuries to the patient. This work arises in order to avoid weaning failures by early detecting the procedure result. This work has as main goal identify possible patient patterns associated to weaning failures. In this context Clustering data mining was used to select and identify the features and the patterns associated to failures. As result an Index-Davies Bouldin of 0.51 was achieved and the most significant variables associated to a failure were identified. The physicians has now new and useful knowledge able to help to take a decision about weaning before it be initiated.por
dc.description.sponsorshipThe authors would like to FCT (Foundation of Science and Technology, Portugal) for the financial support through the contract PTDC/EEI-SII/1302/2012 (INTCare II). This work has been supported by FCT - Fundação para a Ciência e Tecnologia within the Project Scope UID/CEC/00319/2013.por
dc.language.isoengpor
dc.publisherNorth Atlantic University Unionpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876-PPCDTI/126314/PTpor
dc.relationinfo:eu-repo/grantAgreement/FCT/COMPETE/126314/PTpor
dc.relationinfo:eu-repo/grantAgreement/FCT/5876/147280/PTpor
dc.rightsrestrictedAccesspor
dc.subjectClusteringpor
dc.subjectData miningpor
dc.subjectExtubationpor
dc.subjectINTCarepor
dc.subjectIntensive care unitpor
dc.subjectIntensive medicinepor
dc.subjectMechanical ventilationpor
dc.subjectRespiratory diseasespor
dc.subjectVentilation weaningpor
dc.titleClustering data mining models to identify patterns in weaning patient failurespor
dc.typearticlepor
dc.peerreviewedyespor
oaire.citationStartPage183por
oaire.citationEndPage190por
oaire.citationVolume10por
dc.date.updated2019-01-15T16:52:10Z-
dc.description.publicationversioninfo:eu-repo/semantics/publishedVersionpor
sdum.export.identifier5267-
sdum.journalInternational Journal of Biology and Biomedical Engineeringpor
Appears in Collections:CAlg - Artigos em revistas internacionais/Papers in international journals

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