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Neurosci."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This study compared different time series algorithms and different activation functions for training and predicting the success or failure of the extubation model.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The results of this study using four validation methods show that the GRU model and Tanh\u2019s 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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fncom.2024.1456771","type":"journal-article","created":{"date-parts":[[2024,10,4]],"date-time":"2024-10-04T04:33:44Z","timestamp":1728016424000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":6,"title":["Optimizing extubation success: a comparative analysis of time series algorithms and activation functions"],"prefix":"10.3389","volume":"18","author":[{"given":"Kuo-Yang","family":"Huang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ching-Hsiung","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shu-Hua","family":"Chi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying-Lin","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jia-Lang","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2024,10,4]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1109\/EMBC.2018.8512251","article-title":"Recurrent neural network for classification of snoring and non-snoring sound events","author":"Arsenali","year":"2018","journal-title":"Proceedings of the 2018 40th annual international conference of the IEEE engineering in medicine and biology society (EMBC)"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2021.107463","article-title":"Automatic and non-invasive Parkinson\u2019s disease diagnosis and severity rating using LSTM network.","volume":"108","author":"Balaji","year":"2021","journal-title":"Appl. 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