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https://ir.cnu.edu.tw/handle/310902800/32247
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| 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
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| 10.3390-jcm7090240.pdf | | 993Kb | Adobe PDF | 310 | View/Open | | index.html | | 0Kb | HTML | 1446 | View/Open |
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