在中国多个地区的青少年中,基于学习的联合抑郁症预测
Yalan Kuang1, Xiao Liao2, Zekun Jiang3
1West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu 610041, China; Med-X Center for Informatics, Sichuan University, Chengdu 610041, China.
Journal of affective disorders
|October 10, 2024
概括
与本地培训相比,联合学习显著提高了20%的青少年抑郁症预测准确度. 压力,倦怠和睡眠障碍成为早期干预的关键预测因素.
科学领域:
- 心理健康 心理健康
- 机器学习 机器学习
- 青少年心理学 青少年心理学
背景情况:
- 青少年抑郁症会影响情绪和社会福祉,需要早期干预.
- 针对心理健康的集中式机器学习引发了隐私问题,限制了临床使用.
- 联合学习为分析敏感的青少年数据提供了一种保护隐私的替代方案.
研究的目的:
- 评估联合学习在预测青少年抑郁症方面的有效性.
- 使用大规模数据集,确定青少年抑郁症状的关键预测因素.
- 将联合学习成绩与传统的本地培训方法进行比较.
主要方法:
- 利用来自中国成都20个地区的583,405名中高中生数据进行联合学习.
- 收集了个人,家庭和学校因素的数据,涵盖了11种心理现象.
- 将联合培训与当地培训框架进行比较,使用XGBoost模型进行分析.
主要成果:
- 联合学习改善了从0.7544 (局部) 到0.9064.4的接受器操作特征曲线下面的区域,用于抑郁症预测.
- 确定压力,学生倦怠和社会联系是抑郁症症状最重要的预测因素.
- 睡眠成为抑郁症症状中最具影响力的次维度,为早期预测提供了潜力.
结论:
- 联合学习提高了青少年抑郁症预测模型的准确性和隐私性.
- 压力,倦怠和睡眠质量等关键因素对于理解和干预青少年抑郁症至关重要.
- 研究结果支持联合学习的潜力,用于大规模的,隐私意识的心理健康研究和临床应用.
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