Characterization and validation of EHR computable phenotypes for Long COVID using patient-reported symptoms: insights from the nationwide RECOVER program.

doi:10.1093/jamia/ocag115

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Article

Characterization and validation of EHR computable phenotypes for Long COVID using patient-reported symptoms: insights from the nationwide RECOVER program.

Journal of the American Medical Informatics Association : JAMIA - 1 Sept 2026

Castro Victor M, Gainer Vivian, Wattanasin Nich, Cagan Andrew, Holzbach Ana, Chan James, Horwitz Leora, Kenney Rachel, Diaz Ivan, Mandel Hannah, Wuller Shannon, Hornig Mady, O'Brien Lisa, Wylam Andrew, Doster James, Moffitt Richard A, Pfaff Emily, Weiner Mark G, Abedian Sajjad, Koropsak Michael, Parthasarathy Sairam, Razzaghi Hanieh, Manjourides Justin, Karlson Elizabeth W, Murphy Shawn N

Abstract excerpt

OBJECTIVE: Long COVID (LC) remains poorly understood, and there is a critical need for advanced computational tools to better identify and characterize patients. In this study, we use summarized symptom reports by RECOVER-Adult cohort participants linked to EHR data to characterize patients and train a computable phenotype algorithm of LC. MATERIALS AND METHODS: The study included adult participants with linked...

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Topics

Adult
Aged
Female
Humans

Methodology Data sources Editorial policy Privacy Delete account & data About Contact Helena Bioinformatics

Male

Middle Aged

Algorithms

Electronic Health Records

Machine Learning

Patient Reported Outcome Measures

Phenotype

Post-Acute COVID-19 Syndrome

Source

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Identifiers

PMID: 42496643
DOI: 10.1093/jamia/ocag115
PMCID: PMC13580738

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