Unsupervised learning technique identifies bronchiectasis phenotypes with distinct clinical characteristics.

Journal: The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease
Published Date:

Abstract

BACKGROUND: Unsupervised learning technique allows researchers to identify different phenotypes of diseases with complex manifestations.

Authors

  • W-J Guan
    State Key Laboratory of Respiratory Disease, National Clinical Research Centre for Respiratory Disease, Guangzhou Institute of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
  • M Jiang
    College of Veterinary Medicine, Northwest A&F University, Yangling 712100, Shaanxi, China.
  • Y-H Gao
    Department of Respiratory and Critical Care Medicine, First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • H-M Li
    State Key Laboratory of Respiratory Disease, National Clinical Research Centre for Respiratory Disease, Guangzhou Institute of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
  • G Xu
    Guangzhou First People's Hospital, Guangzhou, Guangdong, China.
  • J-P Zheng
    State Key Laboratory of Respiratory Disease, National Clinical Research Centre for Respiratory Disease, Guangzhou Institute of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, Guangzhou, Guangdong, China.
  • R-C Chen
    State Key Laboratory of Respiratory Disease, National Clinical Research Centre for Respiratory Disease, Guangzhou Institute of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, China. chenrc@vip.163.com.
  • N-S Zhong
    State Key Laboratory of Respiratory Disease, National Clinical Research Centre for Respiratory Disease, Guangzhou Institute of Respiratory Disease, First Affiliated Hospital of Guangzhou Medical University, 151 Yanjiang Road, Guangzhou, Guangdong, China. nanshan@vip.163.com.