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.
【저자키워드】 COVID-19, SARS-CoV-2, artificial intelligence, machine learning, Human, Electronic health record, neural networks, 【초록키워드】 knowledge, Symptom, cough, diarrhea, COVID, amplification, PCR testing, Disease diagnosis, Patient, understanding, temporal dynamics, patients, platform, Combination, COVID-19 symptom, anosmia/dysgeusia, Record, intelligence, amplified, identify, significantly, addition, clinical note, 【제목키워드】 Symptom, COVID-19 diagnosis, EHR, reveal, clinical note,