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.

翻译：背景：对临床进展随时间推移的可靠预测可改善抑郁症的预后。目前鲜有研究整合多种抑郁风险因素，以确定最具效用的因素组合，从而识别最高风险个体。方法：本研究证明，基于数据驱动的机器学习方法，如RE-EM（随机效应/期望最大化）树和MERF（混合效应随机森林），可可靠地识别对抑郁症高风险亚组分类最具效用的变量。185名年轻成人完成了抑郁风险测量，包括反刍、担忧、消极认知风格、认知与应对灵活性、消极生活事件以及抑郁症状。我们训练了RE-EM树和MERF算法，并将其与传统线性混合模型进行比较，通过交叉验证前瞻性和同时预测抑郁症状。结果：结果表明，RE-EM树和MERF方法能建模复杂交互作用、识别个体亚组，并预测抑郁症严重程度，其效能与线性混合模型相当。此外，机器学习模型确定沉思、消极生活事件、消极认知风格和感知控制是未来抑郁水平最相关的预测因子。结论：随机效应机器学习模型具有较高的临床效用潜力，可用于制定干预措施以降低抑郁易感性。