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世界范围内迅速上升。为了应对这种上升趋势，临床医生和政策制定者需要知道哪些风险因素在预测青少年心理健康状况不佳时最重要。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取风险因素并复制结果，但难以解释结果，因为这些方法是非理论的。这项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危险因素。机器学习模型检查了 10 岁时评估的 79 个变量中的哪一个是 13 岁和 17 岁青少年心理健康的最重要预测因素。这些模型在来自 9 个国家的 1176 个有青少年的家庭样本中进行了检查。机器学习模型准确地分类了 78% 的 13 岁内化行为高于中位数的青少年、77.3% 的 13 岁外化行为高于中位数的青少年、73.2% 的 17 岁外化行为高于中位数的青少年、60.6% 的 17 岁外化行为高于中位数的青少年。 17 岁时的内化行为高于中位数。 10 岁青少年外化和内化行为的测量是 13 岁和 17 岁外化/内化行为最重要的预测因素，其次是家庭背景变量、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，并准确预测了在收集机器学习模型中使用的数据 3-7 年后，大约十分之七的青少年表现出高于平均水平的心理健康问题。