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ORIGINAL RESEARCH article

Front. Comput. Neurosci.
Volume 18 - 2024 | doi: 10.3389/fncom.2024.1456771
This article is part of the Research Topic Neuroscience's Big Data Revolution View all articles

Optimizing Extubation Success: A Comparative Analysis of Time Series Algorithms and Activation Functions

Provisionally accepted
Kuo-Yang Huang Kuo-Yang Huang 1Ching-Hsiung Lin Ching-Hsiung Lin 1Ying-Lin Hsu Ying-Lin Hsu 2Jia-Lang Xu Jia-Lang Xu 3*
  • 1 Changhua Christian Hospital, Changhua, Taiwan
  • 2 National Chung Hsing University, Taichung, Taiwan
  • 3 Chaoyang University of Technology, Taichung, Taichung County, Taiwan

The final, formatted version of the article will be published soon.

    The success and failure of extubation of patients with acute respiratory failure is a very important issue for clinicians, and the failure of the ventilator often leads to possible complications, which in turn leads to a lot of doubts about the medical treatment in the minds of the people, so in order to increase the success of extubation success of the doctors to prevent the possible complications, the present study compared different time series algorithms and different activation functions for the training and prediction of extubation success or failure models.Methods: This study compared different time series algorithms and different activation functions for training and predicting the success or failure of the extubation model.The results of this study using four validation methods show that the GRU model and Tanh's model have a better predictive model for predicting the success or failure of the extubation and better predictive result of 94.44% can be obtained using Holdout Cross-Validation validation method.This study proposes a prediction method using GRU on the topic of extubation, and it can provide the doctors with the clinical application of extubation to give advice for reference.

    Keywords: time series, deep learning, Extubation, Weaning, Smart Healthcare Dropout: 0.2

    Received: 29 Jun 2024; Accepted: 06 Sep 2024.

    Copyright: © 2024 Huang, Lin, Hsu and Xu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

    * Correspondence: Jia-Lang Xu, Chaoyang University of Technology, Taichung, 41349, Taichung County, Taiwan

    Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.