在大学生中研究与酒精有关的问题的回归树应用
Frank J Schwebel1, Matthew R Pearson1, Dylan K Richards1
1Center on Alcohol, Substance Use, and Addictions, University of New Mexico.
Experimental and clinical psychopharmacology
|May 2, 2024
概括
机器学习确定了大学生中与酒精有关的问题的关键预测因素. 应对抑郁和暴饮频率是最强的指标,指导潜在的精确成治疗.
科学领域:
- 心理学 心理学 心理学
- 数据科学数据科学数据科学
- 成 药物 药物 药物 药物
背景情况:
- 在成治疗中,精准医学正在出现.
- 机器学习 (ML) 应用用于识别酒精问题的预测因素是有限的.
- 递归分区是一种有效的ML方法,用于识别预测因子和临床切断点.
研究的目的:
- 用ML识别大学生中与酒精有关的问题的预测因素和切割点.
- 检查这些预测指标的稳定性,并在两个大型数据集中切割点.
- 为大学生滥用酒精的精准医学方法提供信息.
主要方法:
- 通过R.中的"rpart"包利用递归分区 (回归树).
- 分析了大学生饮酒者的两个大型数据集 (N=5,090和N=2,808).
- 将71个预测因素分为人口,酒精使用和心理社会类别;使用AUDIT和BYACQ评估结果变量.
主要成果:
- 确定了与酒精有关的问题的九个突出的预测因素,包括应对抑郁症,符合性动机,狂饮频率和精神病症状.
- 应对抑郁症 (切割点范围:1.83-2.17) 和狂饮频率 (切割点范围:1.5-2.5) 是最常见的分割变量.
- 结果显示相对稳定,占酒精相关问题的17%-30%的差异.
结论:
- 应对抑郁和过度饮酒的频率是大学生酒精问题干预的关键目标.
- ML识别的预测因素为开发定制,精确的成治疗提供了宝贵的见解.
- 这些发现支持使用ML来确定大学生滥用酒精的预防和治疗的可操作目标.
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