ArticleFrontiers in computational neuroscience2024
Optimizing extubation success: a comparative analysis of time series algorithms and activation functions.
Article in Frontiers in computational neuroscience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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Who cites it
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine learning for diabetic foot care: accuracy trends and emerging directions in healthcare AI.Frontiers in public health · 2025Pooled it
- A comprehensive study based on machine learning models for early identification Mycoplasma pneumoniae infection in segmental/lobar pneumonia.Scientific reports · 2025Article
- Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction.Scientific reports · 2025Article
- Post-surgical fall risk prediction: a machine learning approach for spine and lower extremity procedures.Frontiers in medicine · 2025Article
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Authors and funding
5 authors.
Funding
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Abstract
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.
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