Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.

ABSTRACT:

Background

Stratification of patients with post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies. However, long COVID is incompletely understood and characterised by a wide range of manifestations that are difficult to analyse computationally. Additionally, the generalisability of machine learning classification of COVID-19 clinical outcomes has rarely been tested.

Methods

We present a method for computationally modelling PASC phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Our approach defines a nonlinear similarity function that maps from a feature space of phenotypic abnormalities to a matrix of pairwise

SUBMITTER: Reese JT 

PROVIDER: S-EPMC9769411 | biostudies-literature | 2023 Jan

REPOSITORIES: biostudies-literature