从早期生活因素预测青少年精神病理:一个机器学习教程.
Faizaan Siddique1,2, Brian K Lee1,3
1Department of Epidemiology and Biostatistics, School of Public Health, Drexel University, Philadelphia, PA, United States of America.
机器学习模型可以使用早期生活因素,如家庭史和社会人口统计学来预测青少年精神病理. 这些模型为年轻人风险预测提供了适度的准确性.
科学领域:
- 流行病学 流行病学
- 机器学习 机器学习
- 青少年健康 青少年健康
背景情况:
- 机器学习 (ML) 在流行病学中的实施需要编程专业知识.
- 使用生命早期因素预测青少年精神病理对于早期干预至关重要.
研究的目的:
- 证明ML用于青少年精神病理学风险预测.
- 评估早期生活因素 (产前,家族史,社会人口统计学) 在预测精神病理学的有用性.
主要方法:
- 利用了来自青少年大脑和认知发展 (ABCD) 研究的9643名青少年 (年龄9-10岁) 的数据.
- 采用5ML算法来预测高的儿童行为检查清单 (CBCL) 成绩.
- 评估模型性能使用灵敏度,特异性,F1得分和AUC.
主要成果:
- 弹性网和梯度增强树木表现最好.
- 包括产前和家族史因素在内的模型实现了0.742-0.745.5的AUC.
- 家庭史和社会人口统计学因素是青少年精神病理学的强有力的预测因素.
结论:
- 结合产前,家族史和社会人口统计因素的ML模型可以适度预测青少年精神病理.
- 对模型过拟合和超参数调整的考虑是必不可少的.
- 未来的模型可能会从额外的相关共变量中受益,以提高预测准确度.
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