Interpretable recurrent neural network models for dynamic prediction of the extubation failure risk in patients with invasive mechanical ventilation in the intensive care unit
Abstract Background Clinical decision of extubation is a challenge in the treatment of patient with invasive mechanical ventilation (IMV), since existing extubation protocols are not capable of precisely predicting extubation failure (EF). This study aims to develop and validate interpretable recurr...
Main Authors: | Zhixuan Zeng, Xianming Tang, Yang Liu, Zhengkun He, Xun Gong |
---|---|
Format: | Article |
Language: | English |
Published: |
BMC
2022-09-01
|
Series: | BioData Mining |
Subjects: | |
Online Access: | https://doi.org/10.1186/s13040-022-00309-7 |
Similar Items
-
Prediction of Extubation Failure for Intensive Care Unit Patients Using Light Gradient Boosting Machine
by: Tingting Chen, et al.
Published: (2019-01-01) -
Role of non invasive ventilation in limiting re-intubation after planned extubation
by: Kamel Abd Elaziz Mohamed, et al.
Published: (2013-10-01) -
Weaning and extubation from neonatal mechanical ventilation: an evidenced-based review
by: Razieh Sangsari, et al.
Published: (2022-11-01) -
Explainable machine learning approach to predict extubation in critically ill ventilated patients: a retrospective study in central Taiwan
by: Kai-Chih Pai, et al.
Published: (2022-11-01) -
Use of non-invasive ventilation to facilitate extubation in a patient with amyotrophic lateral sclerosis with hypercapnic respiratory failure
by: Montserrat Diaz-Abad, et al.
Published: (2019-06-01)