Publication: Power index of the inspiratory flow signal as a predictor of weaning in intensive care units
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M. J.F. and J. Kress, “Weaning patients from the ventilator,” The new England Journal of Medicine, vol. 367, pp. 2233–9, 2012.
J-M. Boles, J. Bion, A. Connors , M. Herridge, B. Marsh, C. Melot, R. Pearl, H. Silverman, M. Stanchina, A. Vieillard-Baron, T. Welte 11. “Weaning from mechanical ventilation”. European Respiratory Journal. No. 29: 1033–1056. 2007.
Tobin, M.J., M.J. Mador, S.M. Guenter, R.F. Lodato, M.A. Sackner, “Variability of resting respiratory center drive and timing in healthy subjects”. J. Appl. Physiol., No. 65, pp. 309-317. 1998.
Blackwood, B., Alderdice, F., Burns, K., Cardwell, C., Lavery, G., and O’Halloran, P. (2011). Use of weaning protocols for reducing duration of mechanical ventilation in critically ill adult patients: Cochrane systematic review and meta-analysis. BMJ, 342 (Jan13 2):c7237–c7237.
Burns, K., Meade, M., Lessard, MR Hand, L., Zhou, Q., Keenan, S., and Lellouche, F. (2013). Wean earlier and automatically with new technology (the wean study). a multicenter, pilot randomized controlled trial. Am J Respir Crit Care Med, 187(11):12031211.
H.R. Hemant, J. Chacko, M.K. Singh, “Weaning from mechanical ventilation- current evidence”. Indian Journal of Anaesth; No. 50(6), pp 435-438. 2006.
Casaseca de la Higuera, P., Martín Fernandez, M., & Arbeloa López, C. Weaning from mechanical ventilation: a retrospective analysis leading to a multimodal perspective. IEEE Transaction on biomedical engineering, No. 57(7), pp 1330-1345. 2006.
M.J. Tobin, “Advances in mechanical ventilation”, N. Engl. J. Med.,Vol. 344, N. 26, pp. 1986-1996, 2001.
Santos Lima, E. J. (2013). Respiratory Rate as a Predictor of Weaning Failure from Mechanical Ventilation. Brazilian Journal of Anesthesiology (English Edition), 63(1):1–6.
Stawicki, S. P. (2007). Mechanical ventilation: Weaning and extubation. OPUS 12 Scientist, 1(2):13–16.
J. Chaparro, B. Giraldo, P. Caminal, S. Benito. “Performance of Respiratory Pattern Parameters in Classifiers for Predict Weaning Process”. Engineering in Medicine and Biology Society, IEMBS ’12. 34th Annual International Conference of the IEEE. 2012.
McConville, J. F. and Kress, J. P. (2012). Weaning Patients from the Ventilator. New England Journal of Medicine, 367(23):2233–2239.
Esteban, A., Frutos-Vivar, F., Muriel, A., Ferguson, N. D., Penuelas, O., et al. (2013). Evolution of mortality over time in patients receiving mechanical ventilation. Am J Respir Crit Care Med, 188(2): 220230.
Jiin-Chyr Hsu, Yung-Fu Chen, Hsuan-Hung Lin, Chi-Hsiang Li and Xiaoyi Jiang, “Construction of Prediction Module for Successful Ventilator Weaning”, New Trends in Applied Artificial Intelligence, pp. 766-775, 2007.
Chao DC and Scheinhorn DJ, “Determining the Best Threshold of Rapid Shallow Breathing Index in a Therapist-Implemented PatientSpecific Weaning Protocol”, Respir Care 2007; 52(2):159 –165.
H.Tinsley and S. Brown, “Handbook of applied multivariate statistics and mathematical modeling,” Academic Press, 2000.
Huberty C., “Applied Discriminant Analysis, Whiley Series in Probability and Mathematical Statistics”, Editorial Jhon Wiley & Sons Inc., 1994.
C. Kingsford and S.L Salzberg, “What are decision trees?”.Nat Biotechnol, Vol. 26, No. 9, pp. 1011–1013, 2008.
U. Fayyad, G. Piatetsky-Shapiro, and P. Smyth, “From Data Mining to Knowledge Discovery in Databases”. American Association for Artificial Intelligence, pp. 0738-4602, 1996.
Steinwart I., Chrismann A., “Super Vector Machine, Information Science and Statistics”, Editorial Springer. 2008.
A. Garde, R. Schroeder, A. Voss, P. Caminal, S. Benito and B.F. Giraldo, “Patients on weaning trials classified with support vector machines”, Physiol. Meas. 31, pp. 979–993, 2010.
M. Sokolova, N. Japkowicz, and S. Szpakowicz, “Beyond Accuracy,F-Score and ROC: A Family of Discriminant Measures forPerformance Evaluation,” Advances in Artificial Intelligence pp.1015–1021, 2006.