Generalizable Long COVID Subtypes: Findings from the NIH N3C and RECOVER Programs.

ABSTRACT: Accurate stratification of patients with Post-acute sequelae of SARS-CoV-2 infection (PASC, or long COVID) would allow precision clinical management strategies and could enable more focussed investigation of the molecular pathogenetic mechanisms of this disease. However, the natural history of long COVID is incompletely understood and characterized by an extremely wide range of manifestations that are difficult to analyze computationally. In addition, the generalizability of machine learning classification of COVID-19 clinical outcomes has rarely been tested. We present a method for computationally modeling long COVID phenotype data based on electronic healthcare records (EHRs) and for assessing pairwise phenotypic similarity between patients using semantic similarity. Using unsupervised m

SUBMITTER: Reese J 

PROVIDER: S-EPMC9164456 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature