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https://hdl.handle.net/1822/67830
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Campo DC | Valor | Idioma |
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dc.contributor.author | Peixoto, Ricardo | por |
dc.contributor.author | Ribeiro, Lisete | por |
dc.contributor.author | Portela, Filipe | por |
dc.contributor.author | Santos, Manuel | por |
dc.contributor.author | Rua, Fernando | por |
dc.date.accessioned | 2020-10-28T18:23:32Z | - |
dc.date.available | 2020-10-28T18:23:32Z | - |
dc.date.issued | 2017 | - |
dc.identifier.issn | 1877-0509 | - |
dc.identifier.uri | https://hdl.handle.net/1822/67830 | - |
dc.description.abstract | Every 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.sponsorship | This 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-000026 | por |
dc.language.iso | eng | por |
dc.publisher | Elsevier Science BV | por |
dc.relation | info:eu-repo/grantAgreement/FCT/5876/147280/PT | por |
dc.rights | openAccess | por |
dc.subject | Data Mining | por |
dc.subject | Classification | por |
dc.subject | Interventions | por |
dc.subject | Reinterventions | por |
dc.subject | INTCare | por |
dc.title | Predicting resurgery in intensive care - a data mining approach | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | https://www.sciencedirect.com/science/article/pii/S1877050917317003 | por |
oaire.citationStartPage | 577 | por |
oaire.citationEndPage | 584 | por |
oaire.citationVolume | 113 | por |
dc.date.updated | 2020-10-28T12:00:42Z | - |
dc.identifier.doi | 10.1016/j.procs.2017.08.291 | por |
dc.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | por |
dc.subject.fos | Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática | por |
dc.subject.wos | Science & Technology | - |
sdum.export.identifier | 7408 | - |
sdum.journal | Procedia Computer Science | por |
sdum.conferencePublication | 8TH 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 WORKSHOPS | por |
sdum.bookTitle | 8TH 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 WORKSHOPS | por |
oaire.version | AM | por |
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Ficheiros deste registo:
Ficheiro | Descrição | Tamanho | Formato | |
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2017 - Predicting Resurgery in Intensive Care - A data Mining Approach.pdf | 439,81 kB | Adobe PDF | Ver/Abrir |