• Félix Martín-González, Javier González-Robledo, Fernando Sánchez-Hernández, and María N Moreno-García.
• María N. Moreno-García, University of Salamanca, Department of Computing and Automation, Plaza de los Caídos s/n, 37008 Salamanca, Spain, E-mail: mmg@usal.es.
• Method Inform Med. 2016 May 17; 55 (3): 234-41.

ObjectivesThis paper addresses the problem of decision-making in relation to the administration of noninvasive mechanical ventilation (NIMV) in intensive care units.MethodsData mining methods were employed to find out the factors influencing the success/failure of NIMV and to predict its results in future patients. These artificial intelligence-based methods have not been applied in this field in spite of the good results obtained in other medical areas.ResultsFeature selection methods provided the most influential variables in the success/failure of NIMV, such as NIMV hours, PaCO2 at the start, PaO2 / FiO2 ratio at the start, hematocrit at the start or PaO2 / FiO2 ratio after two hours. These methods were also used in the preprocessing step with the aim of improving the results of the classifiers. The algorithms provided the best results when the dataset used as input was the one containing the attributes selected with the CFS method.ConclusionsData mining methods can be successfully applied to determine the most influential factors in the success/failure of NIMV and also to predict NIMV results in future patients. The results provided by classifiers can be improved by preprocessing the data with feature selection techniques.

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