Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing

• Published in: Journal of the American Medical Informatics Association 2021-12, Vol.29 (1), p.12-21
• Main Authors: Meystre, Stéphane M, Heider, Paul M, Kim, Youngjun, Davis, Matthew, Obeid, Jihad, Madory, James, Alekseyenko, Alexander V
• Format: Article
• Language: English
• Subjects: COVID-19; COVID-19 Testing; Humans; Natural Language Processing; Pandemics; Research and Applications; SARS-CoV-2
• ISSN: 1527-974X; 1067-5027; 1527-974X
• DOI: 10.1093/jamia/ocab186

Abstract Objective The COVID-19 (coronavirus disease 2019) pandemic response at the Medical University of South Carolina included virtual care visits for patients with suspected severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. The telehealth system used for these visits only exports a text note to integrate with the electronic health record, but structured and coded information about COVID-19 (eg, exposure, risk factors, symptoms) was needed to support clinical care and early research as well as predictive analytics for data-driven patient advising and pooled testing. Materials and Methods To capture COVID-19 information from multiple sources, a new data mart and a new natural language processing (NLP) application prototype were developed. The NLP application combined reused components with dictionaries and rules crafted by domain experts. It was deployed as a Web service for hourly processing of new data from patients assessed or treated for COVID-19. The extracted information was then used to develop algorithms predicting SARS-CoV-2 diagnostic test results based on symptoms and exposure information. Results The dedicated data mart and NLP application were developed and deployed in a mere 10-day sprint in March 2020. The NLP application was evaluated with good accuracy (85.8% recall and 81.5% precision). The SARS-CoV-2 testing predictive analytics algorithms were configured to provide patients with data-driven COVID-19 testing advices with a sensitivity of 81% to 92% and to enable pooled testing with a negative predictive value of 90% to 91%, reducing the required tests to about 63%. Conclusions SARS-CoV-2 testing predictive analytics and NLP successfully enabled data-driven patient advising and pooled testing.