SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction

SWIFT: A deep learning approach to prediction of hypoxemic events in critically-Ill patients using SpO2 waveform prediction

Hypoxemia is a significant driver of mortality and poor clinical outcomes in conditions such as brain injury and cardiac arrest in critically ill patients, including COVID-19 patients. Given the host of negative clinical outcomes attributed to hypoxemia, identifying patients likely to experience hyp...

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Bibliographic Details
Main Authors: Annapragada, A.V (Author), Bose, S.N (Author), Greenstein, J.L (Author), Sarma, S.V (Author), Winslow, R.L (Author), Winters, B.D (Author)
Format: Article
Language:English
Published: Public Library of Science 2021
Subjects:
Article
blood
coronavirus disease 2019
COVID-19
critical illness
Critical Illness
critically ill patient
decision support system
deep learning
Deep Learning
epidemiology
human
Humans
hypoxemia
hypoxia
Hypoxia
intensive care unit
Intensive Care Units
isolation and purification
oxygen saturation
Oxygen Saturation
pandemic
Pandemics
pathophysiology
prediction
resource allocation
SARS-CoV-2
SpO2 Waveform ICU Forecasting Technique
virology
Online Access:View Fulltext in Publisher

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