Determination of mode of ventilation using OSRE

作者: D. Faulke , T.A. Etchells , M.J. Harrison , P.J.G. Lisboa

DOI: 10.1016/J.COMPBIOMED.2009.08.004

关键词: Artificial ventilationRespiratory rateIntermittent positive pressure ventilationMedicineAnesthesiaBreathing

摘要: This study classifies the mode of ventilation using respiratory rate, inhaled and exhaled carbon dioxide concentrations in anaesthetised patients. Thirty seven patients were breathing spontaneously (SPONT) 50 on a ventilator (intermittent positive pressure ventilation, IPPV). A data-based methodology for rule inference from trained neural networks, orthogonal search-based extraction, identified two sets low-order Boolean rules differential identification ventilation. Combining both models produced three possible outcomes; IPPV, SPONT 'Uncertain'. The true rates approximately maintained at 96% IPPV 93% SPONT, with false 0.4% each category 4.3% 'Uncertain' inferences.

参考文章(22)
Bhupendra Gohil, Diagnostic alarms in anaesthesia Auckland University of Technology. ,(2007)
A. R. Stark, Y. Sun, I. Kohane, Fuzzy logic assisted control of inspired oxygen in ventilated newborn infants. annual symposium on computer application in medical care. pp. 757- 761 ,(1994)
T. A. Etchells, M. J. Harrison, Orthogonal search‐based rule extraction for modelling the decision to transfuse Anaesthesia. ,vol. 61, pp. 335- 338 ,(2006) , 10.1111/J.1365-2044.2006.04545.X
Silvia Miksch, Werner Horn, Christian Popow, Franz Paky, Utilizing temporal data abstraction for data validation and therapy planning for artificially ventilated newborn infants Artificial Intelligence in Medicine. ,vol. 8, pp. 543- 576 ,(1996) , 10.1016/S0933-3657(96)00355-7
P.J.G Lisboa, A Vellido, H Wong, Bias reduction in skewed binary classification with Bayesian neural networks Neural Networks. ,vol. 13, pp. 407- 410 ,(2000) , 10.1016/S0893-6080(00)00022-8
L. H. J. Eberhart, M. Traeger, A. Eberhart, G. Geldner, A. M. Morin, C. Putzke, H. Wulf, Vorhersage von Übelkeit und Erbrechen in der postoperativen Phase durch ein künstliches neuronales Netz Anaesthesist. ,vol. 52, pp. 1132- 1138 ,(2003) , 10.1007/S00101-003-0575-Y
O. A. P. Santanen, N. Svartling, J. Haasio, M. P. J. Paloheimo, Neural nets and prediction of the recovery rate from neuromuscular block. European Journal of Anaesthesiology. ,vol. 20, pp. 87- 92 ,(2005) , 10.1017/S0265021503000164
Mauricio A. Leon, Ferdinando L. Lorini, Ventilation mode recognition using artificial neural networks Computers and Biomedical Research. ,vol. 30, pp. 373- 378 ,(1997) , 10.1006/CBMR.1997.1452
Geoffrey W. Rutledge, George E. Thomsen, Brad R. Farr, Maria A. Tovar, Jeanette X. Polaschek, Ingo A. Beinlich, Lewis B. Sheiner, Lawrence M. Fagan, The design and implementation of a ventilator-management advisor Artificial Intelligence in Medicine. ,vol. 5, pp. 67- 82 ,(1993) , 10.1016/0933-3657(93)90006-O