Optimization Techniques to Detect Early Ventilation Extubation in Intensive Care Units

  • Pedro Oliveira
  • Filipe PortelaEmail author
  • Manuel F. Santos
  • José Machado
  • António Abelha
  • Álvaro Silva
  • Fernando Rua
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 445)


The decision support models in intensive care units are developed to support medical staff in their decision making process. However, the optimization of these models is particularly difficult to apply due to dynamic, complex and multidisciplinary nature. Thus, there is a constant research and development of new algorithms capable of extracting knowledge from large volumes of data, in order to obtain better predictive results than the current algorithms. To test the optimization techniques a case study with real data provided by INTCare project was explored. This data is concerning to extubation cases. In this dataset, several models like Evolutionary Fuzzy Rule Learning, Lazy Learning, Decision Trees and many others were analysed in order to detect early extubation. The hybrids Decision Trees Genetic Algorithm, Supervised Classifier System and KNNAdaptive obtained the most accurate rate 93.2, 93.1, 92.97 % respectively, thus showing their feasibility to work in a real environment.


Optimization techniques Decision support systems Machine learning Heuristics Intensive care units extubation 


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Pedro Oliveira
    • 1
  • Filipe Portela
    • 1
    • 2
    Email author
  • Manuel F. Santos
    • 1
  • José Machado
    • 1
  • António Abelha
    • 1
  • Álvaro Silva
    • 3
  • Fernando Rua
    • 3
  1. 1.Algoritmi Research CentreUniversity of MinhoGuimarãesPortugal
  2. 2.ESEIGPorto PolytechnicPortoPortugal
  3. 3.Intensive Care UnitCentro Hospitalar do PortoPortoPortugal

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