A Symptom-Based Natural Language Processing Surveillance Pipeline for Post-COVID-19 Patients.
Stud Health Technol Inform
; 310: 860-864, 2024 Jan 25.
Article
en En
| MEDLINE
| ID: mdl-38269931
ABSTRACT
Post-acute sequelae of SARS CoV-2 (PASC) are a group of conditions in which patients previously infected with COVID-19 experience symptoms weeks/months post-infection. PASC has substantial societal burden, including increased healthcare costs and disabilities. This study presents a natural language processing (NLP) based pipeline for identification of PASC symptoms and demonstrates its ability to estimate the proportion of suspected PASC cases. A manual case review to obtain this estimate indicated our sample incidence of PASC (13%) was representative of the estimated population proportion (95% CI 19±6.22%). However, the high number of cases classified as indeterminate demonstrates the challenges in classifying PASC even among experienced clinicians. Lastly, this study developed a dashboard to display views of aggregated PASC symptoms and measured its utility using the System Usability Scale. Overall comments related to the dashboard's potential were positive. This pipeline is crucial for monitoring post-COVID-19 patients with potential for use in clinical settings.
Palabras clave
Texto completo:
1
Bases de datos:
MEDLINE
Asunto principal:
COVID-19
Tipo de estudio:
Diagnostic_studies
/
Guideline
/
Screening_studies
Límite:
Humans
Idioma:
En
Revista:
Stud Health Technol Inform
Asunto de la revista:
INFORMATICA MEDICA
/
PESQUISA EM SERVICOS DE SAUDE
Año:
2024
Tipo del documento:
Article
País de afiliación:
Estados Unidos