An Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units

doi:10.3390/jcm7090240

CNU IR > College of Smart Living > Dept. of Hospital and Health (including master's program) > Periodical Articles > Item 310902800/32247

Please use this identifier to cite or link to this item: https://ir.cnu.edu.tw/handle/310902800/32247

Title:	An Artificial Neural Network Model for Predicting Successful Extubation in Intensive Care Units
Authors:	Hsieh, Meng-Hsuen Hsieh, Meng-Ju Chen, Chin-Ming Hsieh, Chia-Chang Chao, Chien-Ming Lai, Chih-Cheng
Contributors:	Univ Calif Berkeley, Dept Elect Engn & Comp Sci Poznan Univ Med Sci, Dept Med Chia Nan Univ Pharm & Sci, Dept Recreat & Hlth Care Management Chi Mei Med Ctr, Dept Intens Care Med China Med Univ, Childrens Hosp, Dept Pediat Chi Mei Med Ctr, Dept Intens Care Med
Keywords:	predictor successful extubation artificial neural network
Date:	2018-09
Issue Date:	2019-11-15 15:46:42 (UTC+8)
Publisher:	MDPI
Abstract:	Background: Successful weaning from mechanical ventilation is important for patients in intensive care units (ICUs). The aim was to construct neural networks to predict successful extubation in ventilated patients in ICUs. Methods: Data from 1 December 2009 through 31 December 2011 of 3602 patients with planned extubation in Chi-Mei Medical Center's ICUs was used to train and test an artificial neural network (ANN). The input was 37 clinical risk factors, and the output was a failed extubation prediction. Results: One hundred eighty-five patients (5.1%) had a failed extubation. Multivariate analyses revealed that failure was positively associated with therapeutic intervention scoring system (TISS) scores (odds ratio [OR]: 1.814; 95% Confidence Interval [CI]: 1.283-2.563), chronic hemodialysis (OR: 12.264; 95% CI: 8.556-17.580), rapid shallow breathing (RSI) (OR: 2.003; 95% CI: 1.378-2.910), and pre-extubation heart rate (OR: 1.705; 95% CI: 1.173-2.480), but negatively associated with pre-extubation PaO2/FiO(2) (OR: 0.529; 95%: 0.370-0.750) and maximum expiratory pressure (MEP) (OR: 0.610; 95% CI: 0.413-0.899). A multilayer perceptron ANN model with 19 neurons in a hidden layer was developed. The overall performance of this model was F-1: 0.867, precision: 0.939, and recall: 0.822. The area under the receiver operating characteristic curve (AUC) was 0.85, which is better than any one of the following predictors: TISS: 0.58 (95% CI: 0.54-0.62; p < 0.001); 0.58 (95% CI: 0.53-0.62; p < 0.001); and RSI: 0.54 (95% CI: 0.49-0.58; p = 0.097). Conclusions: The ANN performed well when predicting failed extubation, and it will help predict successful planned extubation.
???metadata.dc.relation.uri???:	http://dx.doi.org/10.3390/jcm7090240
Relation:	Catalysts, v.7, n.9, 240
Appears in Collections:	[Dept. of Hospital and Health (including master's program)] Periodical Articles

Files in This Item:

File	Description	Size	Format
10.3390-jcm7090240.pdf		993Kb	Adobe PDF	270	View/Open
index.html		0Kb	HTML	1253	View/Open

Loading...