Ventilator weaning is the process of discontinuing mechanical ventilators
from patients with respiratory failure. This study designed a clinical decision support
system (CDSS) using support vector machine (SVM) to predict if a patient can be weaned
from mechanical ventilator successfully. A lter method based on logistic regression anal-
ysis (LRA) and a wrapper method based on recursive feature elimination (RFE) were
adopted to select salient features in 27 variables, including demographic information,
physiology and disease factors, and care and treatment factors for CDSS. Data of 348
patients were collected at four different periods from all-purpose respiratory care cen-
ter. Seven signi cant variables (p < 0:05) using LRA contrasted to eleven variables
using RFE algorithm were selected. The predictive accuracy under cross-validation is
88.33% (LRA) and 92.73% (RFE), respectively. The systems outperform predictors (75-
78%) built using frequency-to-tidal volume ratio (f=VT ) and a model (78.6%) constructed
recently using a combination of sample entropy of inspiratory tidal volume (VTI), ex-
piratory tidal volume (VTE), and respiration rate (RR). The CDSS constructed using
SVM was shown to have better accuracy (91.25%) than using neural network (88.69%).
Additionally, the designed CDSS with a graphic user interface (GUI) provides a valuable
tool to assist physicians to determine if a patient is ready to wean from the ventilator.
Keywords: Ventilation weaning, Clinical decision support system, Neural network,
Support vector machine, Recursive feature elimination, Logistic regression analysis
關聯:
International Journal of Innovative Computing Information and Control 8(1(B)):933-952
