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Understanding the temporal dynamics of COVID-19 patient phenotypes isnecessary to derive fine-grained resolution of pathophysiology. Here we usestate-of-the-art deep neural networks over an institution-wide machineintelligence platform for the augmented curation of 15.8 million clinical notesfrom 30,494 patients subjected to COVID-19 PCR diagnostic testing. Bycontrasting the Electronic Health Record (EHR)-derived clinical phenotypes ofCOVID-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, weidentify anosmia/dysgeusia (37.4-fold), myalgia/arthralgia (2.6-fold), diarrhea(2.2-fold), fever/chills (2.1-fold), respiratory difficulty (1.9-fold), andcough (1.8-fold) as significantly amplified in COVIDpos over COVIDneg patients.The specific combination of cough and diarrhea has a 3.2-fold amplification inCOVIDpos patients during the week prior to PCR testing, and along withanosmia/dysgeusia, constitutes the earliest EHR-derived signature of COVID-19(4-7 days prior to typical PCR testing date). This study introduces anAugmented Intelligence platform for the real-time synthesis of institutionalknowledge captured in EHRs. The platform holds tremendous potential for scalingup curation throughput, with minimal need for retraining underlying neuralnetworks, thus promising EHR-powered early diagnosis for a broad spectrum ofdiseases.

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