Representation Learning and Spectral Clustering for the Development and External Validation of Dynamic Sepsis Phenotypes: Observational Cohort Study.

ABSTRACT:

Background

Recent attempts at clinical phenotyping for sepsis have shown promise in identifying groups of patients with distinct treatment responses. Nonetheless, the replicability and actionability of these phenotypes remain an issue because the patient trajectory is a function of both the patient's physiological state and the interventions they receive.

Objective

We aimed to develop a novel approach for deriving clinical phenotypes using unsupervised learning and transition modeling.

Methods

Forty commonly used clinical variables from the electronic health record were used as inputs to a feed-forward neural network trained to predict the onset of sepsis. Using spectral clustering on the representations from this network, we derived and validated consistent phenotype

SUBMITTER: Boussina A 

PROVIDER: S-EPMC10337434 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature