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

Journal: Respiratory medicine

PMID: 38710399

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

Javad Razjouyan

Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, USA.
Nicola A Hanania

Section of Pulmonary and Critical Care Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA. Electronic address: Hanania@bcm.edu.
Sara Nowakowski

Center for Innovations in Quality, Effectiveness, and Safety, Michael E. DeBakey VA Medical Center, Houston, TX, USA.
Ritwick Agrawal

Section of Pulmonary and Critical Care Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA; Pulmonary, Critical Care and Sleep Medicine Section, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA.
Amir Sharafkhaneh

Section of Pulmonary and Critical Care Medicine, Department of Medicine, Baylor College of Medicine, Houston, TX, 77030, USA; Pulmonary, Critical Care and Sleep Medicine Section, Michael E. DeBakey VA Medical Center, Houston, TX, 77030, USA. Electronic address: amirs@bcm.edu.

Age Factors Aged Cluster Analysis Cohort Studies Comorbidity Female Humans Longitudinal Studies Machine Learning Male Middle Aged Phenotype Polysomnography Pulmonary Disease, Chronic Obstructive Quality of Life Sleep Sleep Wake Disorders Unsupervised Machine Learning

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