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 …