Huang Kuo-Yang, Lin Ching-Hsiung, Chi Shu-Hua, Hsu Ying-Lin, Xu Jia-Lang
Division of Chest Medicine, Department of Internal Medicine, Changhua Christian Hospital, Changhua, Taiwan.
Institute of Genomics and Bioinformatics, National Chung Hsing University, Taichung, Taiwan.
Front Comput Neurosci. 2024 Oct 4;18:1456771. doi: 10.3389/fncom.2024.1456771. eCollection 2024.
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.
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.
急性呼吸衰竭患者拔管的成功与失败对临床医生来说是一个非常重要的问题，而呼吸机使用失败往往会导致可能的并发症，进而引发人们对医疗救治的诸多质疑。因此，为提高医生拔管的成功率并预防可能的并发症，本研究比较了不同时间序列算法和不同激活函数用于拔管成功或失败模型的训练与预测。
本研究比较了不同时间序列算法和不同激活函数用于训练和预测拔管模型的成功或失败。
本研究采用四种验证方法的结果表明，门控循环单元（GRU）模型和双曲正切（Tanh）模型对拔管成功或失败具有较好的预测模型，使用留出法交叉验证方法可获得94.44%的较好预测结果。
本研究提出了一种基于GRU的拔管预测方法，可为医生在拔管的临床应用中提供参考建议。
