A Symptom-Based Natural Language Processing Surveillance Pipeline for Post-COVID-19 Patients

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...

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Published inStudies in health technology and informatics Vol. 310; p. 860
Main Authors Silverman, Greg M, Rajamani, Geetanjali, Ingraham, Nicholas E, Glover, James K, Sahoo, Himanshu S, Usher, Michael, Zhang, Rui, Ikramuddin, Farha, Melnik, Tanya E, Melton, Genevieve B, Tignanelli, Christopher J
Format Journal Article
LanguageEnglish
Published Netherlands 25.01.2024
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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.
AbstractList 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.
Author Ikramuddin, Farha
Glover, James K
Melnik, Tanya E
Tignanelli, Christopher J
Rajamani, Geetanjali
Melton, Genevieve B
Zhang, Rui
Sahoo, Himanshu S
Ingraham, Nicholas E
Silverman, Greg M
Usher, Michael
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  organization: Department of Surgery, University of Minnesota, Minneapolis, MN, USA
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Snippet 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...
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StartPage 860
SubjectTerms COVID-19 - epidemiology
Disease Progression
Health Care Costs
Humans
Natural Language Processing
SARS-CoV-2
Title A Symptom-Based Natural Language Processing Surveillance Pipeline for Post-COVID-19 Patients
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