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

Vasilis Nikolaou* (Corresponding Author), Sebastiano Massaro, Masoud Fakhimi, Lampros Stergioulas, David Price

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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
Original languageEnglish
Article number106093
Pages (from-to)106093
Number of pages20
JournalRespiratory Medicine
Volume171
Early online date28 Jul 2020
DOIs
Publication statusPublished - Sep 2020

Keywords

  • chronic respiratory disease
  • subtypes
  • statistical analysis
  • Statistical analysis
  • Chronic respiratory disease
  • Subtypes
  • SUBTYPES
  • CLINICAL PHENOTYPES
  • AIRWAYS DISEASE
  • IDENTIFICATION
  • MILD
  • OBSTRUCTIVE PULMONARY-DISEASE
  • DOUBLE-BLIND
  • EXACERBATIONS
  • LUNG-DISEASE
  • ASTHMA

Fingerprint

Dive into the research topics of 'COPD phenotypes and machine learning cluster analysis: A systematic review and future research agenda'. Together they form a unique fingerprint.

Cite this