Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample.

Journal: PloS one
PMID: 27935995

Abstract

BACKGROUND: Depression is commonly comorbid with many other somatic diseases and symptoms. Identification of individuals in clusters with comorbid symptoms may reveal new pathophysiological mechanisms and treatment targets. The aim of this research was to combine machine-learning (ML) algorithms with traditional regression techniques by utilising self-reported medical symptoms to identify and describe clusters of individuals with increased rates of depression from a large cross-sectional community based population epidemiological study.

Authors

  • Joanna F Dipnall
    IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.
  • Julie A Pasco
    IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.
  • Michael Berk
    IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.
  • Lana J Williams
    IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.
  • Seetal Dodd
    IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.
  • Felice N Jacka
    IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.
  • Denny Meyer
    Department of Statistics, Data Science and Epidemiology, Swinburne University of Technology, Melbourne, Victoria, Australia.

Keywords

External Resources

Popular Topics
Recent Journals