Success/Failure Prediction of Noninvasive Mechanical Ventilation in Intensive Care Units. Using Multiclassifiers and Feature Selection Methods.
OBJECTIVE
This paper addresses the problem of decision-making in relation to the administration of noninvasive mechanical ventilation (NIMV) in intensive care units.


METHODS
Data 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.


RESULTS
Feature 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.


CONCLUSIONS
Data 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.
Félix
F
Martín-González
Javier
J
González-Robledo
Fernando
F
Sánchez-Hernández
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.
María N
MN
Moreno-García
D000465:Algorithms; D057225:Data Mining; D019992:Databases as Topic; D003663:Decision Trees; D006801:Humans; D007362:Intensive Care Units; D012121:Respiration, Artificial; D016896:Treatment Outcome
Noninvasive ventilation; classifiers; data mining; feature selection methods; multiclassifiers; respiration disorders; respiratory insufficiency