Risk Factors for Patient–Ventilator Asynchrony and Its Impact on Clinical Outcomes: Analytics Based on Deep Learning Algorithm

Risk Factors for Patient–Ventilator Asynchrony and Its Impact on Clinical Outcomes: Analytics Based on Deep Learning Algorithm

Background and objectives: Patient–ventilator asynchronies (PVAs) are common in mechanically ventilated patients. However, the epidemiology of PVAs and its impact on clinical outcome remains controversial. The current study aims to evaluate the epidemiology and risk factors of PVAs and their impact...

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Bibliographic Details
Main Authors: Huiqing Ge, Kailiang Duan, Jimei Wang, Liuqing Jiang, Lingwei Zhang, Yuhan Zhou, Luping Fang, Leo M. A. Heunks, Qing Pan, Zhongheng Zhang
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-11-01
Series:Frontiers in Medicine
Subjects:
patient ventilator asynchrony
mortality
deep learning
mechanical ventilalion
critical care
Online Access:https://www.frontiersin.org/articles/10.3389/fmed.2020.597406/full

Internet

https://www.frontiersin.org/articles/10.3389/fmed.2020.597406/full

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