Machine learning models predict long COVID outcomes based on baseline clinical and immunologic factors.

ABSTRACT:

Background

The post-acute sequelae of SARS-CoV-2 (PASC), also known as long COVID, remain a significant health issue that is incompletely understood. Predicting which acutely infected individuals will develop long COVID is challenging due to the absence of established biomarkers, clear disease mechanisms, or well-defined sub-phenotypes. Machine learning (ML) models may address this gap by leveraging clinical data to enhance diagnostic precision.

Methods

Clinical data, including antibody titers and viral load measurements collected at the time of hospital admission, are used to predict the likelihood of acute COVID-19 progressing to long COVID. Machine learning models are trained and evaluated for predictive performance. Feature importance analysis is performed to identify th

SUBMITTER: Doni Jayavelu N 

PROVIDER: S-EPMC12764860 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature