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 Liebers; Zainab Doctor; Eli Silvert; Hugo Solomon; Akash Anand; Rakesh Barve; Gregory Gores; Amy W. Williams; William G. Morice; John Halamka; Andrew Badley; Venky Soundararajan

doi:10.7554/eLife.58227

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 journal › Article › peer-review

15 Scopus citations

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.

Original language	English (US)
Article number	e58227
Pages (from-to)	1-12
Number of pages	12
Journal	eLife
Volume	9
DOIs	https://doi.org/10.7554/eLife.58227
State	Published - Jul 2020

ASJC Scopus subject areas

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

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10.7554/eLife.58227

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Wagner, T., Shweta, F. N. U., Murugadoss, K., Awasthi, S., Venkatakrishnan, A. J., Bade, S., Puranik, A., Kang, M., Pickering, B. W., O’horo, J. C., Bauer, P. R., Razonable, R. R., Vergidis, P., Temesgen, Z., Rizza, S., Mahmood, M., Wilson, W. R., Challener, D., Anand, P., ... Soundararajan, V. (2020). Augmented curation of clinical notes from a massive ehr system reveals symptoms of impending covid-19 diagnosis. eLife, 9, 1-12. Article e58227. https://doi.org/10.7554/eLife.58227

Wagner, T, Shweta, FNU, 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, Doctor, Z, Silvert, E, Solomon, H, Anand, A, Barve, R, Gores, G, Williams, AW, Morice, WG, Halamka, J, Badley, A & Soundararajan, V 2020, 'Augmented curation of clinical notes from a massive ehr system reveals symptoms of impending covid-19 diagnosis', eLife, vol. 9, e58227, pp. 1-12. https://doi.org/10.7554/eLife.58227

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

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 (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.",

author = "Tyler Wagner and Shweta, {F. N.U.} and Karthik Murugadoss and Samir Awasthi and Venkatakrishnan, {A. J.} and Sairam Bade and Arjun Puranik and Martin Kang and Pickering, {Brian W.} and O{\textquoteright}horo, {John C.} and Bauer, {Philippe R.} and Razonable, {Raymund R.} and Paschalis Vergidis and Zelalem Temesgen and Stacey Rizza and Maryam Mahmood and Wilson, {Walter R.} and Douglas Challener and Praveen Anand and Matt Liebers and Zainab Doctor and Eli Silvert and Hugo Solomon and Akash Anand and Rakesh Barve and Gregory Gores and Williams, {Amy W.} and Morice, {William G.} and John Halamka and Andrew Badley and Venky Soundararajan",

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year = "2020",

month = jul,

doi = "10.7554/eLife.58227",

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AU - Wagner, Tyler

AU - Shweta, F. N.U.

AU - Murugadoss, Karthik

AU - Awasthi, Samir

AU - Venkatakrishnan, A. J.

AU - Bade, Sairam

AU - Puranik, Arjun

AU - Kang, Martin

AU - Pickering, Brian W.

AU - O’horo, John C.

AU - Bauer, Philippe R.

AU - Razonable, Raymund R.

AU - Vergidis, Paschalis

AU - Temesgen, Zelalem

AU - Rizza, Stacey

AU - Mahmood, Maryam

AU - Wilson, Walter R.

AU - Challener, Douglas

AU - Anand, Praveen

AU - Liebers, Matt

AU - Doctor, Zainab

AU - Silvert, Eli

AU - Solomon, Hugo

AU - Anand, Akash

AU - Barve, Rakesh

AU - Gores, Gregory

AU - Williams, Amy W.

AU - Morice, William G.

AU - Halamka, John

AU - Badley, Andrew

AU - Soundararajan, Venky

PY - 2020/7

Y1 - 2020/7

N2 - 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.

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

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