Augmented curation of clinical notes from a massive EHR system reveals symptoms of impending COVID-19 diagnosis.
Where this comes from
- Record sourced from PubMed, PMID 32633720.
- Also identified by DOI 10.7554/eLife.58227 and PMC identifier 7410498.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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<i><sub>pos</sub></i>; n = 2,317) versus COVID-19-negative (COVID<i><sub>neg</sub></i>; 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<i><sub>pos</sub></i> over COVID<i><sub>neg</sub></i> patients. The combination of cough and fever/chills has 4.2-fold amplification in COVID<i><sub>pos</sub></i> 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 <i>Augmented Intelligence</i> 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.
Medical subject headings
- Clinical Laboratory Techniques
- Coronavirus Infections
- Pneumonia, Viral