Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).

Journal: European psychiatry : the journal of the Association of European Psychiatrists
Published Date: Jan 1, 2017

Abstract

BACKGROUND: Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM).

Authors

  • J F Dipnall
    Impact strategic research centre, school of medicine, Deakin university, PO Box 281, Geelong, Victoria 3220, Australia; Department of statistics, data science and epidemiology, Swinburne university of technology, Swinburne, Australia. Electronic address: jdipnall@deakin.edu.au.
  • J A Pasco
    Impact strategic research centre, school of medicine, Deakin university, PO Box 281, Geelong, Victoria 3220, Australia; Melbourne clinical school-western campus, the university of Melbourne, Saint-Albans, VIC, Australia; Department of epidemiology and preventive medicine, Monash university, Melbourne, VIC, Australia; University hospital of Geelong, Geelong, VIC, Australia.
  • M Berk
    Impact strategic research centre, school of medicine, Deakin university, PO Box 281, Geelong, Victoria 3220, Australia; University hospital of Geelong, Geelong, VIC, Australia; Department of psychiatry, the university of Melbourne, Parkville, VIC, Australia; Florey institute of neuroscience and mental health, Parkville, VIC, Australia; Orygen, the National centre of excellence in youth mental health, Parkville, VIC, Australia.
  • L J Williams
    Impact strategic research centre, school of medicine, Deakin university, PO Box 281, Geelong, Victoria 3220, Australia.
  • S Dodd
    Impact strategic research centre, school of medicine, Deakin university, PO Box 281, Geelong, Victoria 3220, Australia; University hospital of Geelong, Geelong, VIC, Australia; Department of psychiatry, the university of Melbourne, Parkville, VIC, Australia.
  • F N Jacka
    Impact strategic research centre, school of medicine, Deakin university, PO Box 281, Geelong, Victoria 3220, Australia; Department of psychiatry, the university of Melbourne, Parkville, VIC, Australia; Centre for adolescent health, Murdoch children's research institute, Melbourne, Australia; Black Dog institute, Sydney, Australia.
  • D Meyer
    Department of statistics, data science and epidemiology, Swinburne university of technology, Swinburne, Australia.

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