在数字自助订阅者样本中预测大麻使用适度:一项机器学习研究
Marleen I A Olthof1, Lucas A Ramos2, Margriet W van Laar3
1Trimbos Institute, Netherlands Institute of Mental Health and Addiction, Utrecht, the Netherlands; Amsterdam UMC, Department of Psychiatry, University of Amsterdam, Amsterdam, the Netherlands.
Drug and alcohol dependence
|September 18, 2024
概括
机器学习模型在预测使用数字自助工具的个人成功减少大麻时显示出适度的准确性. 关键预测因素包括不识别为大麻使用者和较低的心理痛苦.
科学领域:
- 数字健康干预措施 数字健康干预措施
- 在行为科学中的机器学习.
- 大麻使用障碍研究研究
背景情况:
- 许多人试图通过自助工具独立减少大麻使用.
- 现有的数字工具显示大麻使用减少的效果大小很小.
- 确定成功的预测因素对于优化大麻使用适度策略至关重要.
研究的目的:
- 通过机器学习研究成功减少大麻使用的预测因素.
- 评估机器学习模型在数字自助环境中预测成功的准确性.
主要方法:
- 来自一项随机对照试验的数据分析,比较数字干预 (ICan与教育模块).
- 包括253名旨在减少或戒除大麻使用的参与者.
- 成功的定义是,在6个月的随访期间,每周大麻使用量减少了50%以上.
- 应用机器学习模型 (随机森林,物流回归) 与嵌套的k-fold交叉验证.
主要成果:
- 机器学习模型的预测准确性较低 (AUROC:随机森林为0.61,物流回归为0.57).
- 成功的关键预测因素包括不认定自己是大麻使用者,不使用烟草,高度抑郁症状,高度心理痛苦和高基线大麻使用.
- 总的来说,预测因素与成功之间的关联并不强烈.
结论:
- 机器学习模型在通过数字工具试图减少大麻的个人预测成功时提供了适度的准确性.
- 需要进一步的研究来改善大麻使用适度的预测模型.
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