Journal of Depression and Anxiety
Open Access

Department of Psychiatry, University of Illinois, Chicago, USA

Publications

• Research Article   
  Utilizing Random Effects Machine Learning Algorithms for Identifying Vulnerability to Depression
  Author(s): Runa Bhaumik* and Jonathan Stange
  
  Background: Reliable prediction of clinical progression over time can improve the outcomes of depression. Little work has been done integrating various risk factors for depression, to determine the combinations of factors with the greatest utility for identifying which individuals are at the greatest risk. Materials and methods: This study demonstrates that data-driven Machine Learning (ML) methods such as Random Effects/Expectation Maximization (RE-EM) trees and Mixed Effects Random Forest (MERF) can be applied to reliably identify variables that have the greatest utility for classifying subgroups at greatest risk for depression. 185 young adults completed measures of depression risk, including rumination, worry, negative cognitive styles, cognitive and coping flexibilities and negative life events, along with symptoms of depressio.. View More»
  DOI: 10.35248/2167-1044.23.12.516

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