Using Random Effects Machine Learning Algorithms to Identify
  Vulnerability to Depression
Runa Bhaumik 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.
Method: This study demonstrates that data-driven machine learning (ML) methods
such as RE-EM (Random Effects/Expectation Maximization) trees and MERF (Mixed
Effects Random Forest) 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 depression. We trained RE-EM trees
and MERF algorithms and compared them to traditional linear mixed models (LMMs)
predicting depressive symptoms prospectively and concurrently with
cross-validation. Results: Our results indicated that the RE-EM tree and MERF
methods model complex interactions, identify subgroups of individuals and
predict depression severity comparable to LMM. Further, machine learning models
determined that brooding, negative life events, negative cognitive styles, and
perceived control were the most relevant predictors of future depression
levels. Conclusions: Random effects machine learning models have the potential
for high clinical utility and can be leveraged for interventions to reduce
vulnerability to depression.