对贝叶斯学习模型的验证,以预测大麻使用障碍的风险
Thanthirige Lakshika M Ruberu1, Rajapaksha Mudalige Dhanushka S Rajapaksha1, Mary M Heitzeg2
1Department of Mathematical Sciences, University of Texas at Dallas, Richardson, TX 75080, United States.
Addictive behaviors
|July 14, 2023
概括
一个新的模型可以预测青少年和年轻人的未来大麻使用障碍 (CUD). 外部验证证实其能够识别晚年患CUD高风险的个体.
科学领域:
- 公共卫生 公共卫生
- 成 药物 药物 药物 药物
- 精神病学是一个精神病学.
背景情况:
- 大麻使用障碍 (CUD) 是一个显著且日益增长的公共卫生挑战.
- 早期识别有风险的青少年和年轻人对于干预至关重要.
- 一个贝叶斯逻辑回归模型被开发来预测未来的CUD风险.
研究的目的:
- 对大麻使用障碍 (CUD) 的预测模型进行外部验证.
- 评估模型在识别未来CUD风险青年的表现.
- 评估模型在不同人群中的通用性.
主要方法:
- 在国家数据集上训练的Add Health模型在两个独立的纵向队列 (MLS和CHDS) 上得到了验证.
- 参与者被追踪到大约30岁,以确定CUD诊断.
- 用曲线下的面积 (AUC) 和预期/观察 (E/O) 案例比率来评估模型性能,并探索重新校准.
主要成果:
- 验证队列包括424名 (MLS) 和637名 (CHDS) 参与者.
- 获得的AUC为MLS的0.66和CHDS的0.73.
- 再校准的E/O比率为0.995 (MLS) 和0.999 (CHDS),表明预测和观察病例之间存在很强的一致性.
结论:
- 外部验证支持Add Health模型在识别CUD风险青少年方面的实用性.
- 该模型在不同人群中展示了可靠的预测能力.
- 这种工具可以帮助制定早期干预策略,以预防CUD.
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