Using Random Effects Machine Learning Algorithms to Identify Vulnerability to Depression

TL;DR

This study demonstrates that advanced machine learning algorithms like RE-EM trees and MERF can effectively identify key risk factors and predict depression severity, offering promising tools for targeted interventions in clinical settings.

Contribution

The paper introduces the application of RE-EM trees and MERF algorithms to identify important depression risk factors and predict depression severity, outperforming traditional models.

Findings

RE-EM trees and MERF models predict depression severity comparable to linear mixed models.

Key predictors include brooding, negative life events, and negative cognitive styles.

Machine learning models can identify subgroups at greatest risk for depression.

Abstract

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.