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.

项目概要青春期早期饮酒与以后饮酒的可能性增加相关依赖性、多种药物滥用、受害、行为问题、精神合并症和延迟快速、准确且可靠地预测哪些儿童达到成人里程碑的方法。青少年有早发的风险，可以提高预防干预措施的针对性，并使将资源集中在最令人衰弱且成本最高的案例上，这是一种有前途且尚未开发的方法。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”），一类源自统计学、计算机科学和工程学的技术，旨在构建这些技术与“传统”技术最明显的区别。统计方法（例如普通最小二乘回归）极其强调对未来的预测案例，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早期酗酒的传统方法。该提案将通过直接比较机器学习方法来探索机器学习方法的潜在贡献在大规模、多地点纵向研究中，与传统方法相比，预测性能如果机器学习方法确实明显优于早期饮酒的发展（N = 731）。传统方法，未来的方向可能包括机器学习的开发和实施另一方面，如果机器学习方法不适用的话。优于传统方法，这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期酒精发作的预测分析将调查机器学习方法的性能是否因自然界而异。预测变量的使用、覆盖的年龄跨度以及要预测的结果因此，目前的建议。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算法和一种传统算法（普通逻辑回归）用于预测稍后早发的酒精 (2) 在其余（30%）的数据上测试这六种预测算法，直接比较他们在多种情况下的预测表现。