Validation of a deep learning, value-based care model to predict mortality and comorbidities from ch...
Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19
About this item
Full title
Author / Creator
Pyrros, Ayis , Rodriguez Fernandez, Jorge , Borstelmann, Stephen M , Flanders, Adam , Wenzke, Daniel , Hart, Eric , Horowitz, Jeanne M , Nikolaidis, Paul , Willis, Melinda , Chen, Andrew , Cole, Patrick , Siddiqui, Nasir , Muzaffar, Momin , Muzaffar, Nadir , McVean, Jennifer , Menchaca, Martha , Katsaggelos, Aggelos K , Koyejo, Sanmi and Galanter, William
Publisher
United States: Public Library of Science
Journal title
Language
English
Formats
Publication information
Publisher
United States: Public Library of Science
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More information
Scope and Contents
Contents
We validate a deep learning model predicting comorbidities from frontal chest radiographs (CXRs) in patients with coronavirus disease 2019 (COVID-19) and compare the model's performance with hierarchical condition category (HCC) and mortality outcomes in COVID-19. The model was trained and tested on 14,121 ambulatory frontal CXRs from 2010 to 2019...
Alternative Titles
Full title
Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19
Authors, Artists and Contributors
Author / Creator
Rodriguez Fernandez, Jorge
Borstelmann, Stephen M
Flanders, Adam
Wenzke, Daniel
Hart, Eric
Horowitz, Jeanne M
Nikolaidis, Paul
Willis, Melinda
Chen, Andrew
Cole, Patrick
Siddiqui, Nasir
Muzaffar, Momin
Muzaffar, Nadir
McVean, Jennifer
Menchaca, Martha
Katsaggelos, Aggelos K
Koyejo, Sanmi
Galanter, William
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_99ee46cd04fe476481c8b9f5e2e59932
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_99ee46cd04fe476481c8b9f5e2e59932
Other Identifiers
ISSN
2767-3170
E-ISSN
2767-3170
DOI
10.1371/journal.pdig.0000057
