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Clustering Barotrauma Patients in ICU–A Data Mining Based Approach Using Ventilator Variables

  • Sérgio Oliveira
  • Filipe PortelaEmail author
  • Manuel F. Santos
  • José Machado
  • António Abelha
  • Álvaro Silva
  • Fernando Rua
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9273)

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.

Keywords

Barotrauma Plateau pressure Intensive medicine Data mining Clustering Similarity Correlation 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Sérgio Oliveira
    • 1
  • Filipe Portela
    • 1
    Email author
  • Manuel F. Santos
    • 1
  • José Machado
    • 1
  • António Abelha
    • 1
  • Álvaro Silva
    • 1
  • Fernando Rua
    • 1
  1. 1.Algoritmi CentreUniversity of MinhoBragaPortugal

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