title: Into the Bowels of Depression: Unravelling Medical Symptoms Associated with Depression by Applying Machine-Learning Techniques to a Community Based Population Sample.
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.
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.|Department of Statistics, Data Science and Epidemiology, Swinburne University of Technology, Melbourne, Victoria, Australia.
authors.forename: Joanna F
authors.initials: JF
authors.lastname: Dipnall
authors.affiliation: IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.|Melbourne Clinical School-Western Campus, The University of Melbourne, St Albans, VIC, Australia.|Department of Epidemiology and Preventive Medicine, Monash University, Melbourne, VIC, Australia.|University Hospital Geelong, Geelong VIC Australia.
authors.forename: Julie A
authors.initials: JA
authors.lastname: Pasco
authors.affiliation: IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.|University Hospital 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.
authors.forename: Michael
authors.initials: M
authors.lastname: Berk
authors.affiliation: IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.|University Hospital Geelong, Geelong VIC Australia.
authors.forename: Lana J
authors.initials: LJ
authors.lastname: Williams
authors.affiliation: IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, Australia.|University Hospital Geelong, Geelong VIC Australia.|Department of Psychiatry, The University of Melbourne, Parkville, VIC, Australia.|Orygen, the National Centre of Excellence in Youth Mental Health, Parkville, VIC, Australia.
authors.forename: Seetal
authors.initials: S
authors.lastname: Dodd
authors.affiliation: IMPACT Strategic Research Centre, School of Medicine, Deakin University, Geelong, VIC, 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.
authors.forename: Felice N
authors.initials: FN
authors.lastname: Jacka
authors.affiliation: Department of Statistics, Data Science and Epidemiology, Swinburne University of Technology, Melbourne, Victoria, Australia.
authors.forename: Denny
authors.initials: D
authors.lastname: Meyer
mesh_terms: D000328:Adult; D016000:Cluster Analysis; D019058:Community Networks; D015897:Comorbidity; D003430:Cross-Sectional Studies; D003863:Depression / Q000175:diagnosis* / Q000453:epidemiology / Q000523:psychology; D003866:Depressive Disorder / Q000145:classification / Q000175:diagnosis* / Q000453:epidemiology; D005260:Female; D005767:Gastrointestinal Diseases / Q000175:diagnosis / Q000453:epidemiology; D006801:Humans; D016015:Logistic Models; D000069550:Machine Learning; D008297:Male; D008875:Middle Aged; D009749:Nutrition Surveys / Q000706:statistics & numerical data; D011159:Population Surveillance / Q000379:methods; D057566:Self Report