Natural language processing enabling COVID-19 predictive analytics to support data-driven patient advising and pooled testing.
J Am Med Inform Assoc
; 29(1): 12-21, 2021 12 28.
Article
en En
| MEDLINE
| ID: mdl-34415311
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 ANDMETHODS:
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.Palabras clave
Texto completo:
1
Colección:
01-internacional
Banco de datos:
MEDLINE
Asunto principal:
COVID-19
Tipo de estudio:
Prognostic_studies
/
Risk_factors_studies
Límite:
Humans
Idioma:
En
Revista:
J Am Med Inform Assoc
Asunto de la revista:
INFORMATICA MEDICA
Año:
2021
Tipo del documento:
Article
País de afiliación:
Estados Unidos