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 (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

SUBMITTER: Wagner T 

PROVIDER: S-EPMC7410498 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature