Abstract:
:Understanding temporal dynamics of COVID-19 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 pos ; n = 2,317) versus COVID-19-negative (COVID neg ; 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 pos over COVID neg patients. The combination of cough and fever/chills has 4.2-fold amplification in COVID pos patients during the week prior to PCR testing, in addition to anosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19. This study introduces an Augmented Intelligence 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.
journal_name
Elifejournal_title
eLifeauthors
Wagner T,Shweta F,Murugadoss K,Awasthi S,Venkatakrishnan AJ,Bade S,Puranik A,Kang M,Pickering BW,O'Horo JC,Bauer PR,Razonable RR,Vergidis P,Temesgen Z,Rizza S,Mahmood M,Wilson WR,Challener D,Anand P,Liebers M,Doctdoi
10.7554/eLife.58227subject
Has Abstractpub_date
2020-07-07 00:00:00issn
2050-084Xpii
58227journal_volume
9pub_type
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