Identifying clinically important COPD sub-types using data-driven approaches in primary care population based electronic health records.

ABSTRACT:

Background

COPD is a highly heterogeneous disease composed of different phenotypes with different aetiological and prognostic profiles and current classification systems do not fully capture this heterogeneity. In this study we sought to discover, describe and validate COPD subtypes using cluster analysis on data derived from electronic health records.

Methods

We applied two unsupervised learning algorithms (k-means and hierarchical clustering) in 30,961 current and former smokers diagnosed with COPD, using linked national structured electronic health records in England available through the CALIBER resource. We used 15 clinical features, including risk factors and comorbidities and performed dimensionality reduction using multiple correspondence analysis. We compared the as

SUBMITTER: Pikoula M 

PROVIDER: S-EPMC6472089 | biostudies-literature | 2019 Apr

REPOSITORIES: biostudies-literature