Unsupervised phenotyping of Severe Asthma Research Program participants using expanded lung data.

ABSTRACT:

Background

Previous studies have identified asthma phenotypes based on small numbers of clinical, physiologic, or inflammatory characteristics. However, no studies have used a wide range of variables using machine learning approaches.

Objectives

We sought to identify subphenotypes of asthma by using blood, bronchoscopic, exhaled nitric oxide, and clinical data from the Severe Asthma Research Program with unsupervised clustering and then characterize them by using supervised learning approaches.

Methods

Unsupervised clustering approaches were applied to 112 clinical, physiologic, and inflammatory variables from 378 subjects. Variable selection and supervised learning techniques were used to select relevant and nonredundant variables and address their predictive values,

SUBMITTER: Wu W 

PROVIDER: S-EPMC4038417 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature