Author response: Augmented curation of clinical notes from a massive EHR system reveals symptoms of impending COVID-19 diagnosis
Year: 2020
Type: peer-review
Abstract: Applying deep learning technology for the large-scale curation of symptoms from unstructured EHR clinical notes accurately predicts the differential signals of COVID-19 diagnosis over the week preceding typical PCR testing.
Authors: Tyler Wagner, FNU Shweta, Karthik Murugadoss, Samir Awasthi, AJ Venkatakrishnan, Sairam Bade, Arjun Puranik, Martin Kang, Brian W Pickering, John C O'Horo, Philippe R Bauer, Raymund R Razonable, Paschalis Vergidis, Zelalem Temesgen, Stacey Rizza, Maryam Mahmood, Walter R Wilson, Douglas Challener, Praveen Anand, Matt Liebers, Zainab Doctor, Eli Silvert, Hugo Solomon, Akash Anand, Rakesh Barve, Gregory Gores, Amy W Williams, William G Morice, John Halamka, Andrew Badley, Venky Soundararajan
Institutions: Nference (India), Nference (United States), Mayo Clinic
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W3047986136