Improvement in the Prediction of Ventilator Weaning Outcomes by an Artificial Neural Network in a Medical ICU

• Published in: Respiratory care Vol. 60; no. 11; p. 1560
• Main Authors: Kuo, Hung-Ju, Chiu, Hung-Wen, Lee, Chun-Nin, Chen, Tzu-Tao, Chang, Chih-Cheng, Bien, Mauo-Ying
• Format: Journal Article
• Language: English
• Published: United States 01.11.2015
• Subjects: Aged; Aged, 80 and over; Airway Extubation; Female; Forecasting - methods; Humans; Intensive Care Units; Lung - physiopathology; Male; Middle Aged; Models, Biological; Neural Networks (Computer); Predictive Value of Tests; Random Allocation; Respiration; Respiration, Artificial; Respiratory Function Tests; ROC Curve; Ventilator Weaning; spontaneous breathing trial; artificial neural network; airway extubation; weaning prediction; receiver operating characteristic curve; rapid shallow breathing index

Twenty-five to 40% of patients pass a spontaneous breathing trial (SBT) but fail to wean from mechanical ventilation. There is no single appropriate and convenient predictor or method that can help clinicians to accurately predict weaning outcomes. This study designed an artificial neural network (A...