nference

Augmented curation of unstructured clinical notes from a massive EHR system reveals specific phenotypic signature of impending COVID-19 diagnosis

Abstract: 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.
  • Authors:
  • FNU Shweta1,
  • Karthik Murugadoss2*,
  • Samir Awasthi2,
  • AJ Venkatakrishnan2,
  • Arjun Puranik2,
  • Martin Kang2,
  • Brian W. Pickering1,
  • John C. O’Horo1,
  • Philippe R. Bauer1,
  • Raymund R. Razonable1,
  • Paschalis Vergidis1,
  • Zelalem Temesgen1,
  • Stacey Rizza1,
  • Maryam Mahmood1,
  • Walter R. Wilson1,
  • Douglas Challener1,
  • Praveen Anand2,
  • Matt Liebers2,
  • Zainab Doctor2,
  • Eli Silvert2,
  • Tyler Wagner2,
  • Gregory J. Gores1,
  • Amy W. Williams1,
  • Venky Soundararajan2,
  • Andrew D. Badley1
  • 1 Mayo Clinic, Rochester MN, USA
  • 2nference, Cambridge MA, USA
  • *Joint first authors
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  • Copyright:
  • The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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  • Copyright:
  • The copyright holder for this preprint is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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