Prediction of weaning from mechanical ventilation using Convolutional Neural Networks

Jia, Y., Kaul, C., Lawton, T., Murray-Smith, R. and Habli, I. (2021) Prediction of weaning from mechanical ventilation using Convolutional Neural Networks. Artificial Intelligence in Medicine, 117, 102087. (doi: 10.1016/j.artmed.2021.102087)

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Weaning from mechanical ventilation covers the process of liberating the patient from mechanical support and removing the associated endotracheal tube. The management of weaning from mechanical ventilation comprises a significant proportion of the care of critically ill intubated patients in Intensive Care Units (ICUs). Both prolonged dependence on mechanical ventilation and premature extubation expose patients to an increased risk of complications and increased health care costs. This work aims to develop a decision support model using routinely-recorded patient information to predict extubation readiness. In order to do so, we have deployed Convolutional Neural Networks (CNN) to predict the most appropriate treatment action in the next hour for a given patient state, using historical ICU data extracted from MIMIC-III. The model achieved 86% accuracy and 0.94 area under the receiver operating characteristic curve (AUC-ROC). We also performed feature importance analysis for the CNN model and interpreted these features using the DeepLIFT method. The results of the feature importance assessment show that the CNN model makes predictions using clinically meaningful and appropriate features. Finally, we implemented counterfactual explanations for the CNN model. This can help clinicians understand what feature changes for a particular patient would lead to a desirable outcome, i.e. readiness to extubate.

Item Type:Articles
Keywords:Deep learning, feature importance, mechanical ventilation, ventilator weaning.
Glasgow Author(s) Enlighten ID:Murray-Smith, Professor Roderick and Kaul, Dr Chaitanya
Authors: Jia, Y., Kaul, C., Lawton, T., Murray-Smith, R., and Habli, I.
College/School:College of Science and Engineering > School of Computing Science
Journal Name:Artificial Intelligence in Medicine
ISSN (Online):1873-2860
Published Online:05 May 2021
Copyright Holders:Copyright © 2021 The Authors
First Published:First published in Artificial Intelligence in Medicine 117:102087
Publisher Policy:Reproduced in accordance with the copyright policy of the publisher

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