2022
DOI: 10.4187/respcare.10382
|View full text |Cite
|
Sign up to set email alerts
|

Predicting Failure of Noninvasive Respiratory Support Using Deep Recurrent Learning

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1
1

Citation Types

0
3
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
3

Relationship

0
3

Authors

Journals

citations
Cited by 3 publications
(3 citation statements)
references
References 38 publications
0
3
0
Order By: Relevance
“…Additionally, the ROX index is only validated for one specific type of high flow system and is largely flow dependent, with increases in ROX index when going from 30 to 60 L per minute of flow potentially reflecting higher severity of lung disease rather than effort 112 . More recent work has used deep learning models to develop predictive algorithms to identify patients at risk of requiring mechanical ventilation in hospitalized COVID‐19 patients, 113 and predicting NIRS failure in patients with acute respiratory failure, with very promising results 114 …”
Section: Controversies Of Nirsmentioning
confidence: 99%
“…Additionally, the ROX index is only validated for one specific type of high flow system and is largely flow dependent, with increases in ROX index when going from 30 to 60 L per minute of flow potentially reflecting higher severity of lung disease rather than effort 112 . More recent work has used deep learning models to develop predictive algorithms to identify patients at risk of requiring mechanical ventilation in hospitalized COVID‐19 patients, 113 and predicting NIRS failure in patients with acute respiratory failure, with very promising results 114 …”
Section: Controversies Of Nirsmentioning
confidence: 99%
“…If the classification is performed on multi-labeled data, the softmax layer is employed, and in the case of binary classifying tasks, the logistic (sigmoid) layer is used, accompanied by the gradient-descent techniques [92]. In each epoch (iteration), the CNN adjusts the weight and bias values, aiming to minimize the cross-entropy loss function defined as Equation (9).…”
Section: Convolutional Neural Networkmentioning
confidence: 99%
“…They can effectively aid in detecting and localizing tumors and lesions in lungs and other tissue. Recurrent neural networks are typically employed to analyze time-series data, like apatient's vital signs, historical data and clinical records, to forecast the progression of the condition along with the treatment outcome [8,9]. Machine learning approaches, on the other hand, are successful in classification problems, and they are capable of distinguishing among different respiratory diseases with respect to the symptoms and/or test results [10][11][12].…”
Section: Introductionmentioning
confidence: 99%