- Sérgio Oliveira8,
- Filipe Portela8,
- Manuel F. Santos8,
- José Machado8,
- António Abelha8,
- Álvaro Silva8 &
- …
- Fernando Rua8
Part of the book series:Lecture Notes in Computer Science ((LNAI,volume 9273))
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Abstract
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.
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Authors and Affiliations
Algoritmi Centre, University of Minho, Braga, Portugal
Sérgio Oliveira, Filipe Portela, Manuel F. Santos, José Machado, António Abelha, Álvaro Silva & Fernando Rua
- Sérgio Oliveira
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- Filipe Portela
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- Manuel F. Santos
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- José Machado
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- António Abelha
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- Álvaro Silva
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- Fernando Rua
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Correspondence toFilipe Portela.
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Editors and Affiliations
ISEC - Coimbra Institute of Engineering, Polytechnic Institute of Coimbra, Coimbra, Portugal
Francisco Pereira
CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
Penousal Machado
CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
Ernesto Costa
CIUSC, Department of Informatics Engineering, University of Coimbra, Coimbra, Portugal
Amílcar Cardoso
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Oliveira, S.et al. (2015). Clustering Barotrauma Patients in ICU–A Data Mining Based Approach Using Ventilator Variables. In: Pereira, F., Machado, P., Costa, E., Cardoso, A. (eds) Progress in Artificial Intelligence. EPIA 2015. Lecture Notes in Computer Science(), vol 9273. Springer, Cham. https://doi.org/10.1007/978-3-319-23485-4_13
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