Please use this identifier to cite or link to this item: http://hdl.handle.net/1822/39279

TitleClustering barotrauma patients in ICU–A data mining based approach using ventilator variables
Author(s)Oliveira, Sérgio Manuel Costa
Portela, Filipe
Santos, Manuel Filipe
Machado, José
Abelha, António
Silva, Álvaro
Rua, Fernando
KeywordsBarotrauma
Plateau pressure
Intensive medicine
Data mining
Clustering
Similarity
Correlation
Issue date2015
PublisherSpringer
JournalLecture Notes in Computer Science
Abstract(s)Predicting barotrauma occurrence in intensive care patients is a difficult task. Data Mining modelling can contribute significantly to the identification of patients who will suffer barotrauma. This can be achieved by grouping patient data, considering a set of variables collected from ventilators directly related with barotrauma, and identifying similarities among them. For clustering have been considered k-means and k-medoids algortihms (Partitioning Around Medoids). The best model induced presented a Davies-Bouldin Index of 0.64. This model identifies the variables that have more similarity among the variables monitored by the ventilators and the occurrence of barotrauma.
TypeConference paper
URIhttp://hdl.handle.net/1822/39279
ISBN978-3-319-23484-7
DOI10.1007/978-3-319-23485-4_13
ISSN0302-9743
Publisher versionhttp://link.springer.com/chapter/10.1007%2F978-3-319-23485-4_13
Peer-Reviewedyes
AccessOpen access
Appears in Collections:CAlg - Livros e capítulos de livros/Books and book chapters

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