Identification of sleep phenotypes in COPD using machine learning-based cluster analysis.

ABSTRACT:

Background

Disturbed sleep in patients with COPD impact quality of life and predict adverse outcomes.

Research question

To identify distinct phenotypic clusters of patients with COPD using objective sleep parameters and evaluate the associations between clusters and all-cause mortality to inform risk stratification.

Study design and methods

A longitudinal observational cohort study using nationwide Veterans Health Administration data of patients with COPD investigated for sleep disorders. Sleep parameters were extracted from polysomnography physician interpretation using a validated natural language processing algorithm. We performed cluster analysis using an unsupervised machine learning algorithm (K-means) and examined the association between clusters and mortality

SUBMITTER: Razjouyan J 

PROVIDER: S-EPMC11218872 | biostudies-literature | 2024 Jun

REPOSITORIES: biostudies-literature