PDF(Pubmed)
Abstract：
UNASSIGNED: 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.
UNASSIGNED: 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 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.
UNASSIGNED: 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.
UNASSIGNED: Random effects machine learning models have the potential for high clinical utility and can be leveraged for interventions to reduce vulnerability to depression.
UNASSIGNED: 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 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.
UNASSIGNED: 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.
UNASSIGNED: Random effects machine learning models have the potential for high clinical utility and can be leveraged for interventions to reduce vulnerability to depression.
摘要：
■随着时间的推移可靠的临床进展预测可以改善抑郁症的预后。整合各种抑郁症危险因素的工作很少，确定效用最大的因素组合，以确定哪些个体风险最大。
■这项研究表明，数据驱动的机器学习（ML）方法，如随机效应/期望最大化（RE-EM）树和混合效应随机森林（MERF），可用于可靠地识别对抑郁风险最大的亚组进行分类的最大效用变量。185名年轻人完成了抑郁风险的测量，包括沉思，担心,消极的认知方式，认知和应对灵活性和负面生活事件，还有抑郁症的症状.我们训练了RE-EM树和MERF算法，并将其与传统的线性混合模型（LMM）进行了比较，并通过交叉验证同时预测了抑郁症状。
■我们的结果表明，RE-EM树和MERF方法对复杂的相互作用进行建模，识别个体的亚组并预测与LMM相当的抑郁严重程度。Further,机器学习模型确定了沉思，负面生活事件，消极的认知方式，和感知控制是未来抑郁水平最相关的预测因子。
■随机效应机器学习模型具有高临床效用的潜力，可以用于干预措施以减少对抑郁症的脆弱性。
■这项研究表明，数据驱动的机器学习（ML）方法，如随机效应/期望最大化（RE-EM）树和混合效应随机森林（MERF），可用于可靠地识别对抑郁风险最大的亚组进行分类的最大效用变量。185名年轻人完成了抑郁风险的测量，包括沉思，担心,消极的认知方式，认知和应对灵活性和负面生活事件，还有抑郁症的症状.我们训练了RE-EM树和MERF算法，并将其与传统的线性混合模型（LMM）进行了比较，并通过交叉验证同时预测了抑郁症状。
■我们的结果表明，RE-EM树和MERF方法对复杂的相互作用进行建模，识别个体的亚组并预测与LMM相当的抑郁严重程度。Further,机器学习模型确定了沉思，负面生活事件，消极的认知方式，和感知控制是未来抑郁水平最相关的预测因子。
■随机效应机器学习模型具有高临床效用的潜力，可以用于干预措施以减少对抑郁症的脆弱性。
