Optimizing extubation success: a comparative analysis of time series algorithms and activation functions.

Autor:	Huang KY; Division of Chest Medicine, Department of Internal Medicine, Changhua Christian Hospital, Changhua, Taiwan.; Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan.; Ph.D. Program in Medical Biotechnology, National Chung Hsing University, Taichung, Taiwan., Lin CH; Division of Chest Medicine, Department of Internal Medicine, Changhua Christian Hospital, Changhua, Taiwan.; Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan., Chi SH; Respiratory Therapy Section for Adult, Changhua Christian Hospital, Changhua, Taiwan., Hsu YL; Department of Applied Mathematics, Institute of Statistics, National Chung Hsing University, Taichung, Taiwan., Xu JL; Department of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, Taiwan.
Jazyk:	angličtina
Zdroj:	Frontiers in computational neuroscience [Front Comput Neurosci] 2024 Oct 04; Vol. 18, pp. 1456771. Date of Electronic Publication: 2024 Oct 04 (Print Publication: 2024).
DOI:	10.3389/fncom.2024.1456771
Abstrakt:	Background: 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. Results: 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. Conclusion: 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. Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. (Copyright © 2024 Huang, Lin, Chi, Hsu and Xu.)
Databáze:	MEDLINE
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