A Deep Learning Framework for Predicting Patient Decannulation on Extracorporeal Membrane Oxygenation Devices: Development and Model Analysis Study (Preprint)

Fuller, J., Abramov, A., Mullin, D., Beck, J., Lemaitre, P., & Azizi, E. (2024). A Deep Learning Framework for Predicting Patient Decannulation on Extracorporeal Membrane Oxygenation Devices: Development and Model Analysis Study. JMIR Biomedical Engineering, 9, e48497. https://doi.org/10.2196/48497
Authors:
Joshua Fuller
Alexey Abramov
Dana Mullin
James Beck
Philippe Lemaître
Elham Azizi
Affiliated Authors:
Joshua Fuller
Alexey Abramov
Dana Mullin
James Beck
Philippe Lemaître
Elham Azizi
Columbia Affiliation:
Subjects:
Mechanical Circulatory Support Systems (OpenAlex Topic)
Mechanical Ventilation in Respiratory Failure and ARDS (OpenAlex Topic)
Management of Cardiac Arrest and Resuscitation (OpenAlex Topic)
Author Keywords:
ai
ecmo
vv
artificial intelligence
clinical ai
clinical decision support
dynamic data
extracorporeal membrane oxygenation
health informatics
machine learning
supervised learning
time series
venovenous
elink
Publication Type:
Article
Unique ID:
10.2196/48497
PMID:
38875691
DOI:
10.2196/48497
Journal:
JMIR Biomedical Engineering
Publication Date:
Data Source:
OpenAlex
Source Link:
View OpenAlex Record
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Record Created: