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

12Citations
Citations of this article
61Readers
Mendeley users who have this article in their library.

This article is free to access.

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.

Cite

CITATION STYLE

APA

Meystre, S. M., Heider, P. M., Kim, Y., Davis, M., Obeid, J., Madory, J., & Alekseyenko, A. V. (2022). Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing. Journal of the American Medical Informatics Association, 29(1), 12–21. https://doi.org/10.1093/jamia/ocab186

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free