Unsupervised clustering for sepsis identification in large-scale patient data: a model development and validation study.

ABSTRACT:

Background

Sepsis is a major global health problem. However, it lacks a true reference standard for case identification, complicating epidemiologic surveillance. Consensus definitions have changed multiple times, clinicians struggle to identify sepsis at the bedside, and differing identification algorithms generate wide variation in incidence rates. The two current identification approaches use codes from administrative data, or electronic health record (EHR)-based algorithms such as the Center for Disease Control Adult Sepsis Event (ASE); both have limitations. Here our primary purpose is to report initial steps in developing a novel approach to identifying sepsis using unsupervised clustering methods. Secondarily, we report preliminary analysis of resulting clusters, using identi

SUBMITTER: Li N 

PROVIDER: S-EPMC11925832 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature