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Paper Details

eARDS: A multi-center validation of an interpretable machine learning algorithm of early onset Acute Respiratory Distress Syndrome (ARDS) among critically ill adults with COVID-19.
PLoS One
22
2021
ARDS, Acute Respiratory Distress Syndrome, COVID-, COVID-19, COVID19, O2, oxygen, participants, patients, respiratory failure
Adolescent, Adult, Aged, Aged, 80 and over, COVID-19, Critical Illness, Female, Humans, Machine Learning, Male, Medical Records Systems, Computerized, Middle Aged, Models, Biological, Oxygen, Respiratory Distress Syndrome, Respiratory Rate, Risk Factors, SARS-CoV-2
Author NameAffiliation
Lakshya SinghalEmory University School of Medicine
Yash GargEmory University School of Medicine
Philip YangEmory University School of Medicine
Azade TabaieEmory University School of Medicine
A Ian WongEmory University School of Medicine
Akram MohammedUniversity of Tennessee Health Science Center
Lokesh ChinthalaUniversity of Tennessee Health Science Center
Dipen KadariaUniversity of Tennessee Health Science Center
Amik SodhiUniversity of Tennessee Health Science Center
Andre L HolderEmory University School of Medicine
Annette M EsperEmory University School of Medicine
James M BlumEmory University School of Medicine
James M BlumEmory University School of Medicine
Robert L DavisUniversity of Tennessee Health Science Center
Gari D CliffordEmory University School of Medicine
Gari D CliffordGeorgia Institute of Technology
Greg S MartinEmory University School of Medicine
Rishikesan KamaleswaranEmory University School of Medicine
Rishikesan KamaleswaranGeorgia Institute of Technology
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