Article
Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics.
The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease - 1 Mar 2016
Guan W-J, Jiang M, Gao Y-H, Li H-M, Xu G, Zheng J-P, Chen R-C, Zhong N-S
Abstract excerpt
BACKGROUND: Unsupervised learning technique allows researchers to identify different phenotypes of diseases with complex manifestations. OBJECTIVES: To identify bronchiectasis phenotypes and characterise their clinical manifestations and prognosis. METHODS: We conducted hierarchical cluster analysis to identify clusters that best distinguished clinical characteristics of bronchiectasis. Demographics, lung...
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