Understanding temporal dynamics of COVID-19 patient symptoms could provide fine-grained resolution to guide clinical decision-making. Here, we use deep neural networks over an institution-wide platform for the augmented curation of clinical notes from 77,167 patients subjected to COVID-19 PCR testing. By contrasting Electronic Health Record (EHR)-derived symptoms of COVID-19-positive (COVID; n=2,317) versus COVID-19-negative (COVID; n=74,850) patients for the week preceding the PCR testing date, we identify anosmia/dysgeusia (27.1-fold), fever/chills (2.6-fold), respiratory difficulty (2.2-fold), cough (2.2-fold), myalgia/arthralgia (2-fold), and diarrhea (1.4-fold) as significantly amplified in COVID over COVID patients. The combination of cough and fever/chills has 4.2-fold amplification in COVID patients during the week prior to PCR testing, and along with anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19. This study introduces an platform for the real-time synthesis of institutional biomedical knowledge. The platform holds tremendous potential for scaling up curation throughput, thus enabling EHR-powered early disease diagnosis.© 2020, Wagner et al.
About The Expert
Tyler Wagner
Fnu Shweta
Karthik Murugadoss
Samir Awasthi
A J 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 Md
Venky Soundararajan
References
PubMed