Challenges in Identifying Asthma Subgroups Using Unsupervised Statistical Learning Techniques

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

AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, cilt.188, sa.11, ss.1303-1312, 2013 (SCI İndekslerine Giren Dergi) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 188 Konu: 11
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1164/rccm.201304-0694oc
  • Dergi Adı: AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
  • Sayfa Sayıları: ss.1303-1312

Özet

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.