Deep learning-based clustering robustly identified two classes of sepsis with both prognostic and predictive values.

ABSTRACT:

Background

Sepsis is a heterogenous syndrome and individualized management strategy is the key to successful treatment. Genome wide expression profiling has been utilized for identifying subclasses of sepsis, but the clinical utility of these subclasses was limited because of the classification instability, and the lack of a robust class prediction model with extensive external validation. The study aimed to develop a parsimonious class model for the prediction of class membership and validate the model for its prognostic and predictive capability in external datasets.

Methods

The Gene Expression Omnibus (GEO) and ArrayExpress databases were searched from inception to April 2020. Datasets containing whole blood gene expression profiling in adult sepsis patients were included

SUBMITTER: Zhang Z 

PROVIDER: S-EPMC7658497 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature