Augmented curation of clinical notes from a massive ehr system reveals symptoms of impending covid-19 diagnosis

Tyler Wagner, F. N.U. 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 LiebersZainab Doctor, Eli Silvert, Hugo Solomon, Akash Anand, Rakesh Barve, Gregory Gores, Amy W. Williams, William G. Morice, John Halamka, Andrew Badley, Venky Soundararajan

Research output: Contribution to journalArticlepeer-review

15 Scopus citations


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 (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, 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.

Original languageEnglish (US)
Article numbere58227
Pages (from-to)1-12
Number of pages12
StatePublished - Jul 2020

ASJC Scopus subject areas

  • General Neuroscience
  • General Biochemistry, Genetics and Molecular Biology
  • General Immunology and Microbiology


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