  Understanding the temporal dynamics of COVID-19 patient phenotypes is
necessary to derive fine-grained resolution of pathophysiology. Here we use
state-of-the-art deep neural networks over an institution-wide machine
intelligence platform for the augmented curation of 15.8 million clinical notes
from 30,494 patients subjected to COVID-19 PCR diagnostic testing. By
contrasting the Electronic Health Record (EHR)-derived clinical phenotypes of
COVID-19-positive (COVIDpos, n=635) versus COVID-19-negative (COVIDneg,
n=29,859) patients over each day of the week preceding the PCR testing date, we
identify anosmia/dysgeusia (37.4-fold), myalgia/arthralgia (2.6-fold), diarrhea
(2.2-fold), fever/chills (2.1-fold), respiratory difficulty (1.9-fold), and
cough (1.8-fold) as significantly amplified in COVIDpos over COVIDneg patients.
The specific combination of cough and diarrhea has a 3.2-fold amplification in
COVIDpos patients during the week prior to PCR testing, and along with
anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19
(4-7 days prior to typical PCR testing date). This study introduces an
Augmented Intelligence platform for the real-time synthesis of institutional
knowledge captured in EHRs. The platform holds tremendous potential for scaling
up curation throughput, with minimal need for retraining underlying neural
networks, thus promising EHR-powered early diagnosis for a broad spectrum of
diseases.
