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

Dipnall, J.F., Pasco, J.A., Berk, M., Williams, L.J., Dodd, S., Jacka, F.N. and Meyer, D. 2017, Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM), European psychiatry, vol. 39, pp. 40-50, doi: 10.1016/j.eurpsy.2016.06.003.

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Title Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM)
Author(s) Dipnall, J.F.
Pasco, J.A.ORCID iD for Pasco, J.A. orcid.org/0000-0002-8968-4714
Berk, M.ORCID iD for Berk, M. orcid.org/0000-0002-5554-6946
Williams, L.J.ORCID iD for Williams, L.J. orcid.org/0000-0002-1377-1272
Dodd, S.ORCID iD for Dodd, S. orcid.org/0000-0002-7918-4636
Jacka, F.N.ORCID iD for Jacka, F.N. orcid.org/0000-0002-9825-0328
Meyer, D.
Journal name European psychiatry
Volume number 39
Start page 40
End page 50
Total pages 11
Publisher Elsevier
Place of publication Amsterdam, The Netherlands
Publication date 2017-01
ISSN 1778-3585
Keyword(s) Boosted regression
Machine learning
Summary 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).

METHODS: Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six "lifestyle-environ" variables were used from the National health and nutrition examination study (2009-2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders.

RESULTS: The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, P<0.001) and GLUMM7-1 (OR: 7.88, P<0.001) with depression was found, with significant interactions with those married/living with partner (P=0.001).

CONCLUSION: Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors.
Language eng
DOI 10.1016/j.eurpsy.2016.06.003
Field of Research 119999 Medical and Health Sciences not elsewhere classified
Socio Economic Objective 920410 Mental Health
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, Elsevier
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090260

Document type: Journal Article
Collections: Faculty of Health
School of Medicine
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