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

项目总结 青春期早期饮酒与后来饮酒的可能性增加相关 依赖、滥用多种药物、受害、行为问题、精神并存和延迟 成人里程碑的成就。快速、准确和可靠地预测哪些儿童 而青少年有早发的风险可以改善预防干预的针对性，使 将资源集中在最具破坏性和代价最高的案件上。一种有希望但尚未开发的方法 解决这个预测问题的是机器学习(也称为统计学习、数据挖掘或预测 建模“)，这是一种源于统计学、计算机科学和工程学的技术，它试图建立 数据驱动的预测算法。这些技术与“传统”技术有明显的区别。 统计方法(例如，普通的最小二乘回归)非常重视对未来的预测 案例，而不是对当前数据的解释，因此它们可能比 确定哪些儿童和青少年会过早饮酒的传统方法。这 提案将探索机器学习方法的潜在贡献，方法是直接比较其 在一项大规模、多地点的纵向研究中，对传统方法的预测性能 早发性酒精使用的发展(N=731)。如果机器学习方法的表现确实显著优于 传统的方法，未来的方向可能包括开发和实施机器学习- 基于现实世界使用的筛选方法。另一方面，如果机器学习方法不能 超过传统方法，这将表明，至少在本研究的背景下(即，这些 预测因素、时间线和结果)，机器学习并不能改善早发性酒精的预测 使用。分析将调查机器学习方法的性能是否在自然界中有所不同 预测变量的使用、覆盖的年龄跨度和要预测的结果。因此，目前的提案 使用现有的纵向数据集来实现两个特定目标：(1)训练五种不同的机器学习 预测早发性酒精的算法和一种传统算法(普通Logistic回归) 在数据的子集(70%)中使用。(2)在剩余(30%)的数据上测试这六种预测算法，并 直接比较它们在多个环境中的预测性能。

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.