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Into the bowels of depression: unravelling medical symptoms associated with depression by applying machine-learning techniques to a community based population sample

Dipnall, Joanna F., Pasco, Julie A., Berk, Michael, Williams, Lana J., Dodd, Seetal, Jacka, Felice N. and Meyer, Denny 2016, Into the bowels of depression: unravelling medical symptoms associated with depression by applying machine-learning techniques to a community based population sample, PLoS one, vol. 11, no. 12, pp. 1-19, doi: 10.1371/journal.pone.0167055.

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Title Into the bowels of depression: unravelling medical symptoms associated with depression by applying machine-learning techniques to a community based population sample
Author(s) Dipnall, Joanna F.
Pasco, Julie A.ORCID iD for Pasco, Julie A. orcid.org/0000-0002-8968-4714
Berk, MichaelORCID iD for Berk, Michael orcid.org/0000-0002-5554-6946
Williams, Lana J.ORCID iD for Williams, Lana J. orcid.org/0000-0002-1377-1272
Dodd, SeetalORCID iD for Dodd, Seetal orcid.org/0000-0002-7918-4636
Jacka, Felice N.ORCID iD for Jacka, Felice N. orcid.org/0000-0002-9825-0328
Meyer, Denny
Journal name PLoS one
Volume number 11
Issue number 12
Article ID e0167055
Start page 1
End page 19
Total pages 19
Publisher Public Library of Science (PLoS)
Place of publication San Francisco, Calif.
Publication date 2016-12-09
ISSN 1932-6203
Keyword(s) Science & Technology
Multidisciplinary Sciences
Science & Technology - Other Topics
LOGISTIC-REGRESSION
DIETARY PATTERNS
GUT
METAANALYSIS
MICROBIOME
MECHANISMS
DISORDERS
DISEASE
HEALTH
IMPACT
Summary 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.

METHODS: A multi-staged methodology utilising ML and traditional statistical techniques was performed using the community based population National Health and Nutrition Examination Study (2009-2010) (N = 3,922). A Self-organised Mapping (SOM) ML algorithm, combined with hierarchical clustering, was performed to create participant clusters based on 68 medical symptoms. Binary logistic regression, controlling for sociodemographic confounders, was used to then identify the key clusters of participants with higher levels of depression (PHQ-9≥10, n = 377). Finally, a Multiple Additive Regression Tree boosted ML algorithm was run to identify the important medical symptoms for each key cluster within 17 broad categories: heart, liver, thyroid, respiratory, diabetes, arthritis, fractures and osteoporosis, skeletal pain, blood pressure, blood transfusion, cholesterol, vision, hearing, psoriasis, weight, bowels and urinary.

RESULTS: Five clusters of participants, based on medical symptoms, were identified to have significantly increased rates of depression compared to the cluster with the lowest rate: odds ratios ranged from 2.24 (95% CI 1.56, 3.24) to 6.33 (95% CI 1.67, 24.02). The ML boosted regression algorithm identified three key medical condition categories as being significantly more common in these clusters: bowel, pain and urinary symptoms. Bowel-related symptoms was found to dominate the relative importance of symptoms within the five key clusters.

CONCLUSION: This methodology shows promise for the identification of conditions in general populations and supports the current focus on the potential importance of bowel symptoms and the gut in mental health research.
Language eng
DOI 10.1371/journal.pone.0167055
Field of Research 111799 Public Health and Health Services not elsewhere classified
MD Multidisciplinary
Socio Economic Objective 920499 Public Health (excl. Specific Population Health) not elsewhere classified
HERDC Research category C1 Refereed article in a scholarly journal
ERA Research output type C Journal article
Copyright notice ©2016, The Authors
Free to Read? Yes
Use Rights Creative Commons Attribution licence
Persistent URL http://hdl.handle.net/10536/DRO/DU:30090775

Document type: Journal Article
Collections: School of Medicine
Open Access Collection
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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.