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https://hdl.handle.net/1822/31384
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Campo DC | Valor | Idioma |
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dc.contributor.author | Veloso, Rui | por |
dc.contributor.author | Portela, Filipe | por |
dc.contributor.author | Santos, Manuel | por |
dc.contributor.author | Silva, Álvaro | por |
dc.contributor.author | Rua, Fernando | por |
dc.contributor.author | Abelha, António | por |
dc.contributor.author | Machado, José Manuel | por |
dc.date.accessioned | 2014-11-26T12:38:56Z | - |
dc.date.available | 2014-11-26T12:38:56Z | - |
dc.date.issued | 2014-11 | - |
dc.identifier.issn | 2212-0173 | por |
dc.identifier.uri | https://hdl.handle.net/1822/31384 | - |
dc.description.abstract | Decision making assumes a critical role in the Intensive Medicine. Data Mining is emerging in the clinical area to provide processes and technologies for transforming data into useful knowledge to support clinical decision makers. Appling clustering techniques to the data available on the patients admitted into Intensive Care Units and knowing which ones correspond to readmissions, it is possible to create meaningful clusters that will represent the base characteristics of readmitted patients. Thus, exploring common characteristics it is possible to prevent discharges that will result into readmissions and then improve the patient outcome and reduce costs. Moreover, readmitted patients present greater difficulty to be recovered. In this work it was followed the Stability and Workload Index for Transfer (SWIFT). A subset of variables from SWIFT was combined with the results from laboratory exams, namely the Lactic Acid and the Leucocytes values, in order to create clusters to identify, in the moment of discharge, patients that probably will be readmitted. | por |
dc.language.iso | eng | por |
dc.publisher | Elsevier 1 | por |
dc.rights | openAccess | por |
dc.subject | Clustering | por |
dc.subject | Data mining | por |
dc.subject | Intensive care units | por |
dc.subject | SWIFT | por |
dc.subject | Readmissions | por |
dc.subject | Intensive care | por |
dc.subject | INTCare | por |
dc.subject | Readmission | por |
dc.title | A clustering approach for predicting readmissions in intensive medicine | por |
dc.type | conferencePaper | por |
dc.peerreviewed | yes | por |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S2212017314003740 | por |
sdum.publicationstatus | published | por |
oaire.citationStartPage | 1307 | por |
oaire.citationEndPage | 1316 | por |
oaire.citationIssue | 16 | por |
oaire.citationTitle | Procedia Technology | por |
oaire.citationVolume | 16 | por |
dc.identifier.doi | 10.1016/j.protcy.2014.10.147 | por |
dc.subject.wos | Science & Technology | por |
sdum.journal | Procedia Technology | por |
sdum.conferencePublication | CENTERIS 2014 - CONFERENCE ON ENTERPRISE INFORMATION SYSTEMS / PROJMAN 2014 - INTERNATIONAL CONFERENCE ON PROJECT MANAGEMENT / HCIST 2014 - INTERNATIONAL CONFERENCE ON HEALTH AND SOCIAL CARE INFORMATION SYSTEMS AND TECHNOLOGIES | por |
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Ficheiro | Descrição | Tamanho | Formato | |
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1-s2.0-S2212017314003740-main[1].pdf | Final | 402,03 kB | Adobe PDF | Ver/Abrir |