Predicting Adolescent Depression Using Machine Learning

使用机器学习预测青少年抑郁症

• 批准号: 10322140
• 负责人: Camelia E Hostinar Caudill
• 金额: $ 21.8万
• 依托单位: UNIVERSITY OF CALIFORNIA AT DAVIS
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10322140
• 关键词: 18 year old; Adolescence; Adolescent; Affect; Age; Algorithms; Anxiety Disorders; Area; Award; Behavioral; Biological; Biological Markers; Child; Childhood; Clinical; Cognitive; Complex; Computer software; Crude Extracts; Data; Data Set; Development; Dimensions; Disease; Early Intervention; Early identification; Foundations; Future; Goals; Health Benefit; Heart Diseases; Human; Impairment; Individual; Interview; Life; Longitudinal Studies; Machine Learning; Measures; Medicine; Mental Depression; Mental Health; Mission; Monitor; Mood Disorders; National Institute of Mental Health; Onset of illness; Outcome; Parents; Patient Self-Report; Pattern; Population; Prevalence; Prevention; Probability; Psychopathology; Public Health; Research; Risk; Risk Estimate; Risk Factors; Sample Size; Sampling; Schizophrenia; Screening procedure; Sensitivity and Specificity; Societies; Source; Substance Use Disorder; Suicide; Symptoms; Techniques; Testing; Time; Youth; adverse childhood events; artificial neural network; base; cancer diagnosis; care providers; child depression; clinical diagnosis; clinical diagnostics; clinically significant; computer science; depressive symptoms; design; developmental psychology; early life stress; follow-up; high risk; individualized medicine; innovation; learning algorithm; long short term memory network; longitudinal dataset; machine learning algorithm; machine learning method; maltreatment; open source; pediatric trauma; personalized predictions; postnatal period; precision medicine; prediction algorithm; predictive modeling; preemptive intervention; prenatal; protective factors; psychosocial; public repository; recurrent neural network; speech recognition; success; theories; tool

PROJECT SUMMARY Depression is an impairing and prevalent mood disorder in adolescence, affecting 1 in 6 youth by age 18. So far, the use of conventional statistical approaches has had limited success in delivering tools for accurate individualized prediction of future depression for a specific child. The objective of this project is to build advanced non-linear machine learning algorithms integrating information from multiple sources to deliver accurate, individualized prediction. To accomplish this objective, the research team will use 5,000 variables (biological, cognitive, socio-emotional, environmental) measured on multiple occasions between the prenatal period and age 10 in children from the Avon Longitudinal Study of Parents and Children (ALSPAC, N = 15,636). The goal is to use features from the prenatal period to age 10 to estimate risk of reaching clinical levels of depressive symptoms between the ages of 12 and 18 years old. The research team will pursue two specific aims. The first aim is to build an algorithm for accurate prediction of adolescent depression by using informative features from the prenatal period until age 10 with machine learning methods that capture complex, multi-variate associations. The team will use several techniques, including artificial neural networks that exploit temporal information (recurrent neural networks, Long Short-Term Memory networks) to identify constellations of highly predictive features. Based on early-life stress sensitization theory, the first hypothesis is that features from the prenatal and early postnatal periods (up to age 5) provide greater predictive power than features from ages 6-10. The second aim is to determine if features predicting depression are unique to depression or shared with anxiety disorder and substance use disorder. Machine learning algorithms will predict age 18 clinical diagnoses of depression, anxiety disorder, and substance use disorder. The team will test the second hypothesis that some predictive features will be unique for each disorder and some will be shared across all three disorder types (e.g., childhood trauma). By accomplishing these aims, the research team will devise a clinically useful algorithm to estimate a child’s probability of developing adolescent depression. All software that will be created for this project will be open-source, and made freely available online in public repositories. Algorithms that would allow accurate early identification of children at risk to develop depression during future adolescent years would provide new avenues for preemptive interventions. This would yield enormous public health benefits by prioritizing treatment and shifting developmental trajectories away from eventual disorder for millions of individuals worldwide. To realize the potential of this overall impact on the field and society, predictive models that calculate risk with high sensitivity and specificity in childhood are needed. The proposed project aims to use robust, rigorous machine learning algorithms to take on this challenge.

项目摘要 抑郁症是一种在青少年中普遍存在的损害性情绪障碍，在18岁之前影响六分之一的青少年。所以 到目前为止，使用传统的统计方法在提供准确的统计工具方面取得的成功有限。 对特定儿童未来抑郁症的个性化预测。该项目的目标是建立 先进的非线性机器学习算法整合了来自多个来源的信息， 准确的个性化预测为了实现这一目标，研究小组将使用5,000个变量 （生物，认知，社会情感，环境）在产前和产后之间的多个场合测量 雅芳父母和儿童纵向研究（ALSPAC，N = 10）中的儿童 15，636）。其目标是使用从产前到10岁的特征来估计达到临床的风险。 12至18岁之间的抑郁症状水平。研究小组将追踪两个 明确的目标。第一个目标是建立一个准确预测青少年抑郁的算法， 从产前到10岁的信息特征， 多变量关联该团队将使用多种技术，包括人工神经网络， 时间信息（递归神经网络，长短期记忆网络）来识别星座 具有高度预测性的特征。根据早期生活压力敏感化理论，第一个假设是， 来自产前和产后早期（5岁以下）的特征提供了比来自 6-10岁。第二个目标是确定预测抑郁症的特征是否是抑郁症独有的， 焦虑症和物质使用障碍机器学习算法将预测18岁 抑郁症、焦虑症和物质使用障碍的临床诊断。小组将测试第二个 假设某些预测特征对于每种疾病都是独特的，而有些则是所有疾病共有的。 三种疾病类型（例如，童年创伤）。通过实现这些目标，研究团队将设计一个 临床上有用的算法来估计一个孩子的发展青少年抑郁症的概率。所有软件 将为这个项目创建的将是开源的，并在公共存储库中免费在线提供。 算法将允许准确的早期识别儿童的风险，发展抑郁症在未来 青少年时期将为先发制人的干预提供新的途径。这将产生巨大的公众 通过优先治疗和改变发展轨迹，使其远离最终的疾病， 全球数百万人。为了实现对该领域和社会的整体影响的潜力， 需要在儿童时期以高灵敏度和特异性计算风险的预测模型。拟议 该项目旨在使用强大，严格的机器学习算法来应对这一挑战。