Identifying Children and Teens at Risk for Early Onset Alcohol Use: An Innovative Application of Machine Learning Algorithms to Prevention

识别有早期饮酒风险的儿童和青少年：机器学习算法在预防中的创新应用

基本信息

批准号：
9753696
负责人：
William Ellerbe Pelham III
金额：
$ 4.37万
依托单位：
ARIZONA STATE UNIVERSITY-TEMPE CAMPUS
依托单位国家：
美国
项目类别：
财政年份：
2018
资助国家：
美国
起止时间：
2018-07-30 至 2020-07-12
项目状态：
已结题

项目摘要

PROJECT SUMMARY Early onset of alcohol use during adolescence is associated with increased probability of later alcohol dependence, polydrug abuse, victimization, conduct problems, psychiatric comorbidities, and delayed achievement of adult milestones. Methods that yield rapid, accurate, and reliable predictions of which children and teens are at risk for early onset can improve the targeting of prevention interventions and enable the concentration of resources on the most debilitating and costly cases. One promising and untapped approach to this prediction problem is machine learning (also called “statistical learning,” “data mining,” or “predictive modeling”), a class of techniques arising from statistics, computer science, and engineering that seeks to build data-driven predictive algorithms. These techniques are most noticeably distinguished from “traditional” statistical methods (e.g., ordinary least squares regression) by their extreme emphasis on prediction of future cases, rather than explanation of the current data, and thus they may offer dramatic advantages over traditional approaches to identifying which children and teens will develop early onset alcohol use. This proposal will explore the potential contribution of machine learning methods by directly comparing their predictive performance to that of the traditional approach in a large-scale, multisite longitudinal study of the development of early onset alcohol use (N = 731). If machine learning methods do significantly outperform the traditional approach, future directions might include the development and implementation of machine-learning- based screening methods for real-world use. On the other hand, if machine learning methods do not outperform the traditional approach, this will suggest that at least in the context of the present study (i.e., these predictors, timeline, and outcome), machine learning does not improve the prediction of early onset alcohol use. Analyses will investigate whether the performance of machine learning methods varies across the nature of predictor variables use, the age span covered, and the outcome to be predicted. Thus, the current proposal uses an extant longitudinal dataset to carry out two specific aims: (1) Train five different machine learning algorithms and one traditional algorithm (ordinary logistic regression) for predicting later early onset alcohol use in a subset (70%) of the data. (2) Test these six predictive algorithms on the rest (30%) of the data and directly compare their predictive performance in multiple contexts.

项目摘要青春期期间的早期饮酒发作与以后酒精的可能性增加有关依赖，滥用多药，欺诈，引起问题，精神病合并症和延迟实现成人里程碑。产生快速，准确且可靠的孩子的方法青少年有早发风险可以改善预防干预措施的靶向，并使资源集中在最衰弱和昂贵的情况下。一种诺言和未开发的方法对于这个预测问题是机器学习（也称为“统计学习”，“数据挖掘”或“预测性）建模”），源自统计，计算机科学和工程的一类技术，试图建立数据驱动的预测算法。这些技术最明显地与“传统”区分开统计方法（例如，普通最小二乘回归）的极端强调未来的预测案例，而不是解释当前数据，因此它们可能会提供巨大的优势传统的方法来确定哪些儿童和青少年将早日发作。这建议将通过直接比较机器学习方法的潜在贡献在大规模的多站点纵向研究中，传统方法的预测性能早期饮酒的发展（n = 731）。如果机器学习方法确实胜过传统方法，未来的方向可能包括机器学习的开发和实施 - 基于现实世界使用的筛选方法。另一方面，如果机器学习方法没有胜过传统方法，这将表明至少在本研究的背景下（即这些预测因素，时间表和结果），机器学习并不能改善早期酒精的预测使用。分析将调查机器学习方法的性能在整个性质上是否有所不同预测变量的使用，涵盖的年龄跨度以及要预测的结果。那是当前的提议使用一个超级纵向数据集执行两个具体目标：（1）训练五个不同的机器学习算法和一种传统算法（普通逻辑回归），用于预测早期发作的酒精在数据的子集（70％）中使用。（2）在其余数据（30％）和直接在多种情况下比较他们的预测性能。