A machine learning-based phenotype for long COVID in children: an EHR-based study from the RECOVER program.

ABSTRACT:

Background

As clinical understanding of pediatric Post-Acute Sequelae of SARS CoV-2 (PASC) develops, and hence the clinical definition evolves, it is desirable to have a method to reliably identify patients who are likely to have post-acute sequelae of SARS CoV-2 (PASC) in health systems data.

Methods and findings

In this study, we developed and validated a machine learning algorithm to classify which patients have PASC (distinguishing between Multisystem Inflammatory Syndrome in Children (MIS-C) and non-MIS-C variants) from a cohort of patients with positive SARS-CoV-2 test results in pediatric health systems within the PEDSnet EHR network. Patient features included in the model were selected from conditions, procedures, performance of diagnostic testing, and medications us

SUBMITTER: Lorman V 

PROVIDER: S-EPMC9810222 | biostudies-literature | 2022 Dec

REPOSITORIES: biostudies-literature