Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/31384

TitleA clustering approach for predicting readmissions in intensive medicine
Author(s)Veloso, Rui
Portela, Filipe
Santos, Manuel
Silva, Álvaro
Rua, Fernando
Abelha, António
Machado, José Manuel
KeywordsClustering
Data mining
Intensive care units
SWIFT
Readmissions
Intensive care
INTCare
Readmission
Issue dateNov-2014
PublisherElsevier
JournalProcedia Technology
Abstract(s)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.
TypeConference paper
URIhttp://hdl.handle.net/1822/31384
DOI10.1016/j.protcy.2014.10.147
ISSN2212-0173
Publisher versionhttp://www.sciencedirect.com/science/article/pii/S2212017314003740
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Livros e capítulos de livros/Books and book chapters

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