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

doi:10.1016/j.rmed.2024.107641

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Article

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

Respiratory medicine - 1 Jun 2024

Razjouyan Javad, Hanania Nicola A, Nowakowski Sara, Agrawal Ritwick, Sharafkhaneh Amir

Abstract excerpt

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...

Topics

Humans
Pulmonary Disease, Chronic Obstructive
Cluster Analysis
Machine Learning
Male
Female
Phenotype
Aged
Longitudinal Studies
Middle Aged
Sleep Wake Disorders

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Polysomnography

Sleep

Comorbidity

Quality of Life

Unsupervised Machine Learning

Age Factors

Cohort Studies

Source

Open in PubMed Open full text

Identifiers

PMID: 38710399
DOI: 10.1016/j.rmed.2024.107641
PMCID: PMC11218872

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 29 Aug 2026. Prepared record publication-38710399-2026-08-29.1. Manifest publication-graph-cohort-2026-08-29.1.

Source receipt: PubMed via Helena Literature Corpus, retrieved 29 Aug 2026.

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