Characterization of long COVID temporal sub-phenotypes by distributed representation learning from electronic health record data: a cohort study.

ABSTRACT:

Background

Characterizing Post-Acute Sequelae of COVID (SARS-CoV-2 Infection), or PASC has been challenging due to the multitude of sub-phenotypes, temporal attributes, and definitions. Scalable characterization of PASC sub-phenotypes can enhance screening capacities, disease management, and treatment planning.

Methods

We conducted a retrospective multi-centre observational cohort study, leveraging longitudinal electronic health record (EHR) data of 30,422 patients from three healthcare systems in the Consortium for the Clinical Characterization of COVID-19 by EHR (4CE). From the total cohort, we applied a deductive approach on 12,424 individuals with follow-up data and developed a distributed representation learning process for providing augmented definitions for PAS

SUBMITTER: Dagliati A 

PROVIDER: S-EPMC10511779 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature