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Methods Inf Med 2016; 55(03): 234-241
DOI: 10.3414/ME14-01-0015
Original Articles
Schattauer GmbH

Success/Failure Prediction of Noninvasive Mechanical Ventilation in Intensive Care Units[*]

Using Multiclassifiers and Feature Selection Methods
Félix Martín-González
1   Intensive Care Unit, University Hospital of Salamanca, Salamanca, Spain
,
Javier González-Robledo
1   Intensive Care Unit, University Hospital of Salamanca, Salamanca, Spain
,
Fernando Sánchez-Hernández
2   School of Nursing and Physiotherapy, University of Salamanca, Prehospital Emergency Services, Salamanca, Spain
,
María N. Moreno-García
3   Department of Computing and Automation, University of Salamanca, Salamanca, Spain
› Author Affiliations
Further Information

Publication History

received: 28 January 2014

accepted: 18 March 2014

Publication Date:
08 January 2018 (online)

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Summary

Objectives: This paper addresses the problem of decision-making in relation to the administration of noninvasive mechanical ventila tion (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.

Keywords

Noninvasive ventilation - respiration disorders - respiratory insufficiency - data mining - feature selection methods - classifiers - multi-classifiers

* Supplementary material published on our web-site http://dx.doi.org/10.3414/ME14-01-0015


 

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