Understanding the importance of key risk factors in predicting chronic bronchitic symptoms using a machine learning approach.

Journal: BMC medical research methodology
Published Date: Mar 29, 2019

Abstract

BACKGROUND: Chronic respiratory symptoms involving bronchitis, cough and phlegm in children are underappreciated but pose a significant public health burden. Efforts for prevention and management could be supported by an understanding of the relative importance of determinants, including environmental exposures. Thus, we aim to develop a prediction model for bronchitic symptoms.

Authors

  • Huiyu Deng
    Department of Preventive Medicine, University of Southern California, 2001 N. Soto Street, MC-9234, Los Angeles, CA, 90089, USA.
  • Robert Urman
    Department of Preventive Medicine, University of Southern California, 2001 N. Soto Street, MC-9234, Los Angeles, CA, 90089, USA.
  • Frank D Gilliland
    Department of Preventive Medicine, University of Southern California, 2001 N. Soto Street, MC-9234, Los Angeles, CA, 90089, USA.
  • Sandrah P Eckel
    Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, California, USA, 90033.

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