Challenges in Identifying Asthma Subgroups Using Unsupervised Statistical Learning Techniques

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Prosperi M. C. F., ŞAHİNER Ü. M., Belgrave D., SAÇKESEN C., Buchan I. E., Simpson A., ...More

AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, vol.188, no.11, pp.1303-1312, 2013 (SCI-Expanded, Scopus)

• Publication Type: Article / Article
• Volume: 188 Issue: 11
• Publication Date: 2013
• Doi Number: 10.1164/rccm.201304-0694oc
• Journal Name: AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
• Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
• Page Numbers: pp.1303-1312
• Keywords: asthma, children, clustering, machine learning, endotypes, CLUSTER-ANALYSIS, EXERCISE, CHILDREN, PHENOTYPES, MIXTURE, SELECTION, SYMPTOMS, TREE
• Open Archive Collection: AVESIS Open Access Collection
• Hacettepe University Affiliated: Yes

Abstract

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.