优化拔管成功率:时间序列算法和激活函数的比较分析。

IF 2.1 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Computational Neuroscience Pub Date : 2024-10-04 eCollection Date: 2024-01-01 DOI:10.3389/fncom.2024.1456771
Kuo-Yang Huang, Ching-Hsiung Lin, Shu-Hua Chi, Ying-Lin Hsu, Jia-Lang Xu
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引用次数: 0

摘要

背景:对于临床医生来说,急性呼吸衰竭患者的拔管成功与否是一个非常重要的问题,而呼吸机的失灵往往会导致可能出现的并发症,进而导致人们心中对医疗产生诸多疑虑,因此为了提高医生的拔管成功率,防止可能出现的并发症,本研究比较了不同时间序列算法和不同激活函数对拔管成功或失败模型的训练和预测:本研究比较了用于训练和预测拔管成功或失败模型的不同时间序列算法和不同激活函数:本研究使用四种验证方法的结果表明,GRU 模型和 Tanh's 模型在预测拔管成败方面具有较好的预测模型,使用 Holdout 交叉验证验证方法可获得 94.44% 的较好预测结果:本研究提出了一种以拔管为主题的GRU预测方法,可为医生提供拔管的临床应用建议,以供参考。
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Optimizing extubation success: a comparative analysis of time series algorithms and activation functions.

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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来源期刊
Frontiers in Computational Neuroscience
Frontiers in Computational Neuroscience MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
5.30
自引率
3.10%
发文量
166
审稿时长
6-12 weeks
期刊介绍: Frontiers in Computational Neuroscience is a first-tier electronic journal devoted to promoting theoretical modeling of brain function and fostering interdisciplinary interactions between theoretical and experimental neuroscience. Progress in understanding the amazing capabilities of the brain is still limited, and we believe that it will only come with deep theoretical thinking and mutually stimulating cooperation between different disciplines and approaches. We therefore invite original contributions on a wide range of topics that present the fruits of such cooperation, or provide stimuli for future alliances. We aim to provide an interactive forum for cutting-edge theoretical studies of the nervous system, and for promulgating the best theoretical research to the broader neuroscience community. Models of all styles and at all levels are welcome, from biophysically motivated realistic simulations of neurons and synapses to high-level abstract models of inference and decision making. While the journal is primarily focused on theoretically based and driven research, we welcome experimental studies that validate and test theoretical conclusions. Also: comp neuro
期刊最新文献
A combinatorial deep learning method for Alzheimer's disease classification-based merging pretrained networks. Multi-scale asynchronous correlation and 2D convolutional autoencoder for adolescent health risk prediction with limited fMRI data. Decoding the application of deep learning in neuroscience: a bibliometric analysis. Optimizing extubation success: a comparative analysis of time series algorithms and activation functions. Editorial: Understanding and bridging the gap between neuromorphic computing and machine learning, volume II.
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