Precision phenotyping for curating research cohorts of patients with unexplained post-acute sequelae of COVID-19.

ABSTRACT:

Background

Scalable identification of patients with post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms, which has led to suboptimal accuracy, demographic biases, and underestimation of the PASC.

Methods

In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying cohorts of patients with PASC. We used longitudinal electronic health records data from over 295,000 patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to simultaneously exclude sequelae that prior conditions can explain and include infection-associated chronic conditions. We performed independent chart reviews to tune and validate the algor

SUBMITTER: Azhir A 

PROVIDER: S-EPMC11911085 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature