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J Clin Med. 2018 Aug 25;7(9). pii: E240. doi: 10.3390/jcm7090240.

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

Author information

1
Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA 94720, USA. emersonhsieh@berkeley.edu.
2
Department of Medicine, Poznan University of Medical Science, 61-701 Poznan, Poland. 76519@student.ump.edu.pl.
3
Department of Recreation and Health Care Management, Chia Nan University of Pharmacy and Science, Tainan 71710, Taiwan. chencm3383@yahoo.com.tw.
4
Department of Intensive Care Medicine, Chi Mei Medical Center, 901 Chung Hwa Road, Yang Kang City 71044, Taiwan. chencm3383@yahoo.com.tw.
5
Department of Pediatrics, China Medical University Children's Hospital, China Medical University, Taichung 40402, Taiwan. d8336@mail.cmuh.org.tw.
6
Department of Intensive Care Medicine, Chi Mei Medical Center, Liouying District, Tainan 73657, Taiwan. ccm870958@yahoo.com.tw.
7
Department of Intensive Care Medicine, Chi Mei Medical Center, Liouying District, Tainan 73657, Taiwan. dtmed141@gmail.com.

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 1/12/2009 through 31/12/2011 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/FiO₂ (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 F₁: 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.

KEYWORDS:

artificial neural network; predictor; successful extubation

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