在青春期和早期成年期使用贝叶斯机器学习对大麻使用障碍的绝对风险预测
Tingfang Wang1, Joseph M Boden2, Swati Biswas1
1Department of Mathematical Sciences, University of Texas at Dallas, Richardson, USA.
Drug and alcohol review
|June 23, 2025
概括
一个新的机器学习模型利用性别,犯罪率和个性特征等因素预测青少年大麻使用障碍 (CUD) 的风险. 该工具有助于识别有风险的年轻人,并制定早期干预策略.
科学领域:
- 成研究研究成研究
- 机器学习在公共卫生中的应用
- 青少年心理学 青少年心理学
背景情况:
- 药物使用障碍 (SUD) 是美国的一个重大公共卫生问题.
- 青少年使用物质可能导致成人SUD,需要早期干预.
- 大麻使用障碍 (CUD) 在青少年和年轻人中越来越令人担忧.
研究的目的:
- 开发和验证青少年和年轻成年人CUD绝对风险预测模型.
- 确定与CUD发展相关的关键风险因素.
- 为临床医生提供一个工具,以评估个人CUD风险.
主要方法:
- 一个贝叶斯式机器学习模型被训练使用从青少年到成人健康的全国纵向研究的数据.
- 该模型预测了个人化CUD对使用大麻的青少年和年轻人的绝对风险.
- 使用五倍交叉验证 (AUC,E/O) 和独立验证数据集来评估性能.
主要成果:
- 该模型确定了五个关键的风险因素:生物性,犯罪,良心,神经病症和开放性.
- 该模型展示了良好的区分和校准,AUC值在0.64到0.75之间,E/O值接近1跨数据集.
- 该模型准确地预测了在第一次使用大麻5年内CUD风险.
结论:
- 开发的模型可以帮助临床医生识别患CUD高风险的青少年和年轻人.
- 早期风险评估使得及时和有针对性的临床干预成为可能.
- 这种预测工具支持公共卫生努力,以减轻大麻使用障碍的进展.
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