Assessing the added value of linking electronic health records to improve the prediction of self-rep...
Assessing the added value of linking electronic health records to improve the prediction of self-reported COVID-19 testing and diagnosis
About this item
Full title
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Publisher
United States: Public Library of Science
Journal title
Language
English
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Publisher
United States: Public Library of Science
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Scope and Contents
Contents
Since the beginning of the Coronavirus Disease 2019 (COVID-19) pandemic, a focus of research has been to identify risk factors associated with COVID-19-related outcomes, such as testing and diagnosis, and use them to build prediction models. Existing studies have used data from digital surveys or electronic health records (EHRs), but very few have...
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Full title
Assessing the added value of linking electronic health records to improve the prediction of self-reported COVID-19 testing and diagnosis
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TN_cdi_plos_journals_2694381406
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_plos_journals_2694381406
Other Identifiers
ISSN
1932-6203
E-ISSN
1932-6203
DOI
10.1371/journal.pone.0269017
