COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda

Nikolaou, Vasilis, Massaro, Sebastiano, Fakhimi, Masoud, Stergioulas, Lampros and Price, David (2020) COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda Journal of Respiratory Medicine, 106093.

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Abstract

Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research

Item Type:	Article
Divisions :	Faculty of Arts and Social Sciences > Surrey Business School
Authors :	Name Email ORCID Nikolaou, Vasilis v.nikolaou@surrey.ac.uk Massaro, Sebastiano s.massaro@surrey.ac.uk Fakhimi, Masoud masoud.fakhimi@surrey.ac.uk Stergioulas, Lampros l.stergioulas@surrey.ac.uk Price, David
Date :	28 July 2020
DOI :	10.1016/j.rmed.2020.106093
Copyright Disclaimer :	© 2020 Elsevier Ltd. All rights reserved.
Uncontrolled Keywords :	Chronic respiratory disease; Subtypes; Statistical analysis;
Additional Information :	Embargo OK Metadata OK No Further Action
Depositing User :	James Marshall
Date Deposited :	28 Jul 2020 10:29
Last Modified :	06 Aug 2020 10:38
URI:	http://epubs.surrey.ac.uk/id/eprint/858310

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