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TitleOptimization techniques to detect early ventilation extubation in intensive care units
Author(s)Oliveira, Pedro Miguel Martins de
Portela, Filipe
Santos, Manuel
Machado, José Manuel
Abelha, António
Silva, Álvaro
Rua, Fernando
KeywordsOptimization techniques
Decision support systems
Machine learning
Intensive care units extubation
Issue date2016
PublisherSpringer Verlag
JournalAdvances in Intelligent Systems and Computing
CitationOliveira, P., Portela, F., Santos, M. F., Machado, J., Abelha, A., Silva, Á., & Rua, F. (2016). Optimization Techniques to Detect Early Ventilation Extubation in Intensive Care Units. In New Advances in Information Systems and Technologies (pp. 599-608). Springer International Publishing.
Abstract(s)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 hydrids 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.
TypeConference paper
Publisher version
AccessOpen access
Appears in Collections:CAlg - Livros e capítulos de livros/Books and book chapters

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