Juan (Wendy) Zhao, PhD

Research Assistant Professor

Using NLP on unstructured EHR for COVID-19 surveillance

Objective: Identifying symptoms highly specific to COVID-19 would improve the clinical and public health response to infectious outbreaks. Here, we describe a high-throughput approach – Concept-Wide Association Study (ConceptWAS) that systematically scans a disease's clinical manifestations from clinical notes. We used this method to identify symptoms specific to COVID-19 early in the course of the pandemic.
Methods: Using the Vanderbilt University Medical Center (VUMC) EHR, we parsed clinical notes through a natural language processing pipeline to extract clinical concepts. We examined the difference in concepts derived from the notes of COVID-19-positive and COVID-19-negative patients on the PCR testing date. We performed ConceptWAS using the cumulative data every two weeks for early identify specific COVID-19 symptoms. 
Results: We processed 87,753 notes 19,692 patients (1,483 COVID-19-positive) subjected to COVID-19 PCR testing between March 8, 2020, and May 27, 2020.  We identified symptoms associated with increasing risk of COVID-19, including “absent sense of smell” (odds ratio [OR] = 4.97, 95% confidence interval [CI] = 3.21–7.50), “fever” (OR = 1.43, 95% CI = 1.28–1.59), “with cough fever” (OR = 2.29, 95% CI = 1.75–2.96), and “ageusia” (OR = 5.18, 95% CI = 3.02–8.58). Using ConceptWAS, we were able to detect loss of sense of smell or taste three weeks prior to their inclusion as symptoms of the disease by the Centers for Disease Control and Prevention (CDC). 


ConceptWAS: A high-throughput method for early identification of COVID-19 presenting symptoms and characteristics from clinical notes

Juan Zhao, Monika E. Grabowska, Vern Eric Kerchberger, Joshua C. Smith, H. Nur Eken, QiPing Feng, Josh F. Peterson, S. Trent Rosenbloom, Kevin B. Johnson, Wei-Qi Wei

Journal of Biomedical Informatics, vol. 117, 2021 May, p. 103748


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