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., ...Daha Fazla

AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE, cilt.188, sa.11, ss.1303-1312, 2013 (SCI-Expanded, Scopus)

• Yayın Türü: Makale / Tam Makale
• Cilt numarası: 188 Sayı: 11
• Basım Tarihi: 2013
• Doi Numarası: 10.1164/rccm.201304-0694oc
• Dergi Adı: AMERICAN JOURNAL OF RESPIRATORY AND CRITICAL CARE MEDICINE
• Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
• Sayfa Sayıları: ss.1303-1312
• Anahtar Kelimeler: asthma, children, clustering, machine learning, endotypes, CLUSTER-ANALYSIS, EXERCISE, CHILDREN, PHENOTYPES, MIXTURE, SELECTION, SYMPTOMS, TREE
• Açık Arşiv Koleksiyonu: AVESİS Açık Erişim Koleksiyonu
• Hacettepe Üniversitesi Adresli: Evet

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.