Augmented curation of clinical notes from a massive EHR system reveals symptoms of impending COVID 19 diagnosis
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.
eLife