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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
› Institutsangaben
Weitere Informationen

Publikationsverlauf

received: 28. Januar 2014

accepted: 18. März 2014

Publikationsdatum:
08. Januar 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


 

  • References

  • 1 Peek N, Swift S. Intelligent Data Analysis for Knowledge Discovery, Patient Monitoring and Quality Assessment. Methods Inf Med 2012; 51 (Suppl. 04) 318-322.
    Thieme ConnectPubMedSuche in Google Scholar
  • 2 Wróbel L, Sikora M. Censoring Weighted Separate-and-Conquer Rule Induction from Survival Data. Methods Inf Med 2014; 53 (Suppl. 02) 137-148.
    Thieme ConnectPubMedSuche in Google Scholar
  • 3 Paoin W. Lesson Learned from Data Mining of WHO Mortality Database. Methods Inf Med 2011; 50 (Suppl. 04) 380-385.
    Thieme ConnectPubMedSuche in Google Scholar
  • 4 Bellazzi R, Diomidous M, Sarkar IN, Takabayashi K, Ziegler A, McCray AT. Data Analysis and Data Mining: Current Issues in Biomedical Informatics. Methods Inf Med, 2011; 50 (Suppl. 04) 536-544.
    Thieme ConnectPubMedSuche in Google Scholar
  • 5 Spreckelsen C, Spitzer K, Honekamp W. Present Situation and Prospect of Medical Knowledge Based Systems in German-speaking Countries. Methods Inf Med 2012; 51 (Suppl. 04) 536-544.
    PubMedSuche in Google Scholar
  • 6 Beuscart R. Know Thyself. Methods Inf Med 2013; 52 (Suppl. 06) 536-537.
    Thieme ConnectPubMedSuche in Google Scholar
  • 7 Tobin MJ. Mechanical ventilation. N Engl J Med 1994; 330 (Suppl. 15) 1056-1061.
    CrossrefPubMedSuche in Google Scholar
  • 8 Tobin MJ. editor. Principles & Practice of Mechanical Ventilation. 2nd ed. McGraw-Hill; 2006
    Suche in Google Scholar
  • 9 Carrillo A, Gonzalez-Diaz G, Ferrer M, Martinez-Quintana ME, Lopez-Martinez A, Llamas N. et al. Non-Invasive ventilation in community-acquired pneumonia and severe acute respiratory failure. Intensive Care Med 2012; 38 (Suppl. 03) 458-466.
    CrossrefPubMedSuche in Google Scholar
  • 10 Nava S, Hill NS. Non-Invasive ventilation in acute respiratory failure. The Lancet 2009; 374 9685 250-259.
    CrossrefPubMedSuche in Google Scholar
  • 11 Nava S, Ceriana P. Causes of Failure of Noninvasive Mechanical Ventilation. Respiratory Care 2004; 49 (Suppl. 03) 2095-2303.
    PubMedSuche in Google Scholar
  • 12 Lightowler JV, Wedzicha JA, Elliott MW, Ram FS. Non-Invasive positive pressure ventilation to treat respiratory failure resulting from exacerbations of chronic obstructive pulmonary disease: Cochrane systematic review and meta-analysis. BMJ 2003; 326 7382 185.
    CrossrefPubMedSuche in Google Scholar
  • 13 Shirakabe A, Hata N, Yokoyama S, Shinada T, Kobayashi N, Tomita K, Kitamura M, Nozaki A, Tokuyama H, Asai K, Mizuno K. Predicting the success of noninvasive positive pressure ventilation in emergency room for patients with acute heart failure. Journal of Cardiology 2011; 57: 107-114.
    CrossrefPubMedSuche in Google Scholar
  • 14 Martí S, Pallero M, Ferrer J, Ríos J, Rodríguez E, Morell F, Muñoz X. Predictors of mortality in chest wall disease treated with noninvasive home mechanical ventilation. Respiratory Medicine 2010; 104: 1843-1849.
    CrossrefPubMedSuche in Google Scholar
  • 15 Babuskaa R, Alica L, Lourensb MS, Verbraak AFM, Bogaard J. Estimation of respiratory parameters via fuzzy clustering. Artificial Intelligence in Medicine 2001; 21: 91-105.
    CrossrefPubMedSuche in Google Scholar
  • 16 Li Q, Lin N. The Bayesian Elastic Net. Bayesian Analysis 2010; 5 (Suppl. 01) 151-170.
    CrossrefPubMedSuche in Google Scholar
  • 17 Montero MA, Ruíz R, García-Torres M, Sarro LM. Feature Selection Applied to Data from the Sloan Digital Sky Survey. Trends in Applied Intelligent Systems. Lecture Notes in Computer Science Volume 6096. 2010 pp 611-620.
    PubMedSuche in Google Scholar
  • 18 Hall MA. Correlation-based Feature Selection for Machine Learning. PhD Thesis, University of Waikato Hamilton, New Zealand: 1999
    PubMedSuche in Google Scholar
  • 19 Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH. The WEKA Data Mining Software: An Update. SIGKDD Explorations 2009; 11 (Suppl. 01) 10-18.
    CrossrefPubMedSuche in Google Scholar
  • 20 Quinlan JR. C4.5: Programs for Machine Learning. San Mateo, CA, USA: Morgan Kaufmann; 1993
    Suche in Google Scholar
  • 21 Cooper GF, Herskovits E. A Bayesian Method for the induction of probabilistic networks from data. Machine Learning 1992; 9: 309-347.
    PubMedSuche in Google Scholar
  • 22 Aha D, Kibler D. Instance-based learning algorithms. Machine Learning 1991; 6: 37-66.
    PubMedSuche in Google Scholar
  • 23 Breiman L. Bagging predictors. Machine Learning 1996; 24 (Suppl. 02) 123-140.
    PubMedSuche in Google Scholar
  • 24 Freund Y, Schapire RE. Experiments with a new boosting algorithm. Proceedings 13th International Conference on Machine Learning. 1996 pp 148-156.
    PubMedSuche in Google Scholar
  • 25 Breiman L. Random Forests. Machine Learning 2001; 45 (Suppl. 01) 5-32.
    CrossrefPubMedSuche in Google Scholar
  • 26 Wolpert DH. Stacked Generalization. Neural Networks 1992; 5: 241-259.
    CrossrefPubMedSuche in Google Scholar
  • 27 Gama J, Brazdil P. Cascade Generalization. Machine Learning 2000; 41 (Suppl. 03) 315-343.
    CrossrefPubMedSuche in Google Scholar
  • 28 Honrubia T, García López FJ, Franco N, Mas M, Guevara M, Daguerre M. et al. Noninvasive vs conventional mechanical ventilation in acute respiratory failure: A multicenter, randomized controlled trial. Chest 2005; 128 (Suppl. 06) 3916-3924.
    CrossrefPubMedSuche in Google Scholar
  • 29 Schettino G, Altobelli N, Kacmarek RM. Noninvasive positive-pressure ventilation in acute respiratory failure outside clinical trials: Experience at the Massachusetts General Hospital. Crit Care Med 2008; 36 (Suppl. 02) 441-447.
    CrossrefPubMedSuche in Google Scholar
  • 30 Bower KM. The Paired t-Test. Scientific Computing and Instrumentation 2001; 18 (Suppl. 03) 62-63.
    PubMedSuche in Google Scholar
  • 31 Guyon I, Elisseeff A. An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 2003; 3: 1157-1182.
    PubMedSuche in Google Scholar