AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, vol.188, no.11, pp.1303-1312, 2013 (Journal Indexed in SCI)
Article / Article
Title of Journal :
AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
asthma, children, clustering, machine learning, endotypes, CLUSTER-ANALYSIS, EXERCISE, CHILDREN, PHENOTYPES, MIXTURE, SELECTION, SYMPTOMS, TREE
Rationale: Unsupervised statistical learning techniques, such as exploratory factor analysis (EFA) and hierarchical clustering (HC), have been used to identify asthma phenotypes, with partly consistent results. Some of the inconsistency is caused by the variable selection and demographic and clinical differences among study populations.