Department of Computer Science, University of California - Davis, Davis, USA.
Genome Center, University of California - Davis, Davis, USA.
Sci Rep. 2024 Oct 7;14(1):23282. doi: 10.1038/s41598-024-72158-9.
Depression is a major cause of disability and mortality for young people worldwide and is typically first diagnosed during adolescence. In this work, we present a machine learning framework to predict adolescent depression occurring between ages 12 and 18 years using environmental, biological, and lifestyle features of the child, mother, and partner from the child's prenatal period to age 10 years using data from 8467 participants enrolled in the Avon Longitudinal Study of Parents and Children (ALSPAC). We trained and compared several cross-sectional and longitudinal machine learning techniques and found the resulting models predicted adolescent depression with recall (0.59 ± 0.20), specificity (0.61 ± 0.17), and accuracy (0.64 ± 0.13), using on average 39 out of the 885 total features (4.4%) included in the models. The leading informative features in our predictive models of adolescent depression were female sex, parental depression and anxiety, and exposure to stressful events or environments. This work demonstrates how using a broad array of evidence-driven predictors from early in life can inform the development of preventative decision support tools to assist in the early detection of risk for mental illness.
抑郁症是全世界导致年轻人残疾和死亡的主要原因，通常在青少年时期首次被诊断出来。在这项工作中，我们提出了一个机器学习框架，使用来自儿童的产前到 10 岁的儿童、母亲和伴侣的环境、生物和生活方式特征，以及来自 8467 名参与阿冯纵向研究父母和孩子（ALSPAC）的参与者的数据，来预测 12 至 18 岁青少年的抑郁情况。我们训练和比较了几种横截面和纵向机器学习技术，发现所得到的模型预测青少年抑郁的召回率（0.59±0.20）、特异性（0.61±0.17）和准确性（0.64±0.13）较高，使用了模型中包含的 885 个总特征（4.4%）中的平均 39 个特征。我们青少年抑郁预测模型中的主要信息特征是女性性别、父母的抑郁和焦虑以及暴露于压力事件或环境中。这项工作表明，如何使用来自生命早期的大量基于证据的预测因子，可以为预防决策支持工具的开发提供信息，以帮助早期发现精神疾病的风险。
