Paper Info
Title
Optimization Techniques to Detect Early Ventilation Extubation in Intensive Care Units.
Abstract
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.
Year
DOI
Venue
2016
10.1007/978-3-319-31307-8_62
NEW ADVANCES IN INFORMATION SYSTEMS AND TECHNOLOGIES, VOL 2
Keywords
Field
DocType
Optimization techniques,Decision Support Systems,Machine Learning,Heuristics,Intensive Care Units Extubation
Decision tree,Simulation,Computer science,Decision support system,Lazy learning,Heuristics,Artificial intelligence,Intensive care,Decision-making,Machine learning,Genetic algorithm,Fuzzy rule
Conference
Volume
ISSN
Citations 
445
2194-5357
1
PageRank 
References 
Authors
0.37
14
7
Name
Order
Citations
PageRank
Pedro Oliveira1133.74
Filipe Portela217744.10
Manuel Filipe Santos336068.91
José Machado48332.46
António Abelha524357.30
Álvaro M. Silva612518.39
Fernando Rua77815.32