Paper Info
Title
Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing
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.
Year
DOI
Venue
2021
10.1093/jamia/ocab186
JOURNAL OF THE AMERICAN MEDICAL INFORMATICS ASSOCIATION
Keywords
DocType
Volume
medical informatics [L01.313.500], natural language processing (nlp) [L01.224.050.375.580], machine learning [g17.035.250.500], data science [L01.305]
Journal
29
Issue
ISSN
Citations 
1
1067-5027
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Stéphane M Meystre100.34
Paul M. Heider202.70
YoungJun Kim3629.08
Matthew Davis400.34
Jihad S. Obeid542.11
James Madory600.34
Alexander V. Alekseyenko7379.10