Crowd-sourced machine learning prediction of long COVID using data from the National COVID Cohort Collaborative.

ABSTRACT:

Background

While many patients seem to recover from SARS-CoV-2 infections, many patients report experiencing SARS-CoV-2 symptoms for weeks or months after their acute COVID-19 ends, even developing new symptoms weeks after infection. These long-term effects are called post-acute sequelae of SARS-CoV-2 (PASC) or, more commonly, Long COVID. The overall prevalence of Long COVID is currently unknown, and tools are needed to help identify patients at risk for developing long COVID.

Methods

A working group of the Rapid Acceleration of Diagnostics-radical (RADx-rad) program, comprised of individuals from various NIH institutes and centers, in collaboration with REsearching COVID to Enhance Recovery (RECOVER) developed and organized the Long COVID Computational Challenge (L3C), a co

SUBMITTER: Bergquist T 

PROVIDER: S-EPMC11462169 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature