Duke University, Durham, NC, USA.
University of Miami, Coral Gables, FL, USA.
J Youth Adolesc. 2023 Aug;52(8):1595-1619. doi: 10.1007/s10964-023-01767-w. Epub 2023 Apr 19.
Adolescent mental health problems are rising rapidly around the world. To combat this rise, clinicians and policymakers need to know which risk factors matter most in predicting poor adolescent mental health. Theory-driven research has identified numerous risk factors that predict adolescent mental health problems but has difficulty distilling and replicating these findings. Data-driven machine learning methods can distill risk factors and replicate findings but have difficulty interpreting findings because these methods are atheoretical. This study demonstrates how data- and theory-driven methods can be integrated to identify the most important preadolescent risk factors in predicting adolescent mental health. Machine learning models examined which of 79 variables assessed at age 10 were the most important predictors of adolescent mental health at ages 13 and 17. These models were examined in a sample of 1176 families with adolescents from nine nations. Machine learning models accurately classified 78% of adolescents who were above-median in age 13 internalizing behavior, 77.3% who were above-median in age 13 externalizing behavior, 73.2% who were above-median in age 17 externalizing behavior, and 60.6% who were above-median in age 17 internalizing behavior. Age 10 measures of youth externalizing and internalizing behavior were the most important predictors of age 13 and 17 externalizing/internalizing behavior, followed by family context variables, parenting behaviors, individual child characteristics, and finally neighborhood and cultural variables. The combination of theoretical and machine-learning models strengthens both approaches and accurately predicts which adolescents demonstrate above average mental health difficulties in approximately 7 of 10 adolescents 3-7 years after the data used in machine learning models were collected.
青少年心理健康问题在全球范围内迅速上升。为了应对这一上升趋势，临床医生和政策制定者需要知道哪些风险因素对预测青少年心理健康不良最重要。理论驱动的研究已经确定了许多预测青少年心理健康问题的风险因素，但很难提炼和复制这些发现。数据驱动的机器学习方法可以提炼风险因素并复制发现，但由于这些方法是无理论的，因此很难解释发现。本研究展示了如何整合数据和理论驱动的方法来识别预测青少年心理健康的最重要的青春期前风险因素。机器学习模型检查了在 10 岁时评估的 79 个变量中哪些是预测 13 岁和 17 岁青少年心理健康的最重要因素。这些模型在来自九个国家的 1176 个青少年家庭样本中进行了检查。机器学习模型准确地将 78%的 13 岁时内部化行为高于中位数的青少年、77.3%的 13 岁时外化行为高于中位数的青少年、73.2%的 17 岁时外化行为高于中位数的青少年和 60.6%的 17 岁时内部化行为高于中位数的青少年进行了分类。青少年外化和内化行为的 10 岁时测量是预测 13 岁和 17 岁外化/内化行为的最重要因素，其次是家庭环境变量、养育行为、儿童个体特征，最后是邻里和文化变量。理论和机器学习模型的结合加强了这两种方法，并准确预测了大约 70%的青少年在使用机器学习模型收集数据后 3-7 年内表现出高于平均水平的心理健康困难。
