Learning competing risks across multiple hospitals: one-shot distributed algorithms.

ABSTRACT:

Objectives

To characterize the complex interplay between multiple clinical conditions in a time-to-event analysis framework using data from multiple hospitals, we developed two novel one-shot distributed algorithms for competing risk models (ODACoR). By applying our algorithms to the EHR data from eight national children's hospitals, we quantified the impacts of a wide range of risk factors on the risk of post-acute sequelae of SARS-COV-2 (PASC) among children and adolescents.

Materials and methods

Our ODACoR algorithms are effectively executed due to their devised simplicity and communication efficiency. We evaluated our algorithms via extensive simulation studies as applications to quantification of the impacts of risk factors for PASC among children and adolescents using

SUBMITTER: Zhang D 

PROVIDER: S-EPMC11031234 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature