Augmented Curation of Unstructured Clinical Notes from a Massive EHR System Reveals Specific Phenotypic Signature of Impending COVID-19 Diagnosis

Shweta, FNU; Murugadoss, Karthik; Awasthi, Samir; Venkatakrishnan, AJ; Puranik, Arjun; Kang, Martin; Pickering, Brian W.; O'Horo, John C.; Bauer, Philippe R.; Razonable, Raymund R.; Vergidis, Paschalis; Temesgen, Zelalem; Rizza, Stacey; Mahmood, Maryam; Wilson, Walter R.; Challener, Douglas; Anand, Praveen; Liebers, Matt; Doctor, Zainab; Silvert, Eli; Solomon, Hugo; Wagner, Tyler; Gores, Gregory J.; Williams, Amy W.; Halamka, John; Soundararajan, Venky; Badley, Andrew D.

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.