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 J Gores , Amy W Williams , William G Morice , John Halamka , Andrew D Badley , Venky Soundararajan
April 23 2020
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 (COVIDpos; n=2,317) versus COVID-19-negative (COVIDneg; 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 COVIDpos over COVIDneg patients. The combination of cough and fever/chills has 4.2-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. 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.