机器学习为年轻人带有压力生活事件的心理健康风险预测:一个建模研究研究
Hexiao Ding1, Na Li2, Lishan Li2
1School of Nursing, Sun Yat-Sen University, No. 74, 2nd Yat-Sen Rd, Yuexiu District, Guangzhou City, Guangdong Province, China; Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hunghom, Hong Kong SAR, China.
Journal of affective disorders
|September 21, 2024
概括
机器学习模型通过识别关键风险因素,如情绪调节和社会支持,有效地预测年轻人的心理健康结果. 这有助于制定有针对性的干预措施,以更好地应对应对策略.
科学领域:
- 精神病学是一个精神病学.
- 计算心理学 计算心理学
- 公共卫生 公共卫生
背景情况:
- 年轻人的心理健康受到压力生活事件的重大影响,特别是在COVID-19大流行期间.
- 未成熟的应对机制可能会加剧青少年和年轻人的负面心理健康结果.
研究的目的:
- 开发和验证用于预测青少年心理健康结果的机器学习模型.
- 在人口,心理社会和情绪智力因素中确定心理健康状况的关键预测因素.
主要方法:
- 对3038名中国年轻人 (14-25岁) 进行了两波横截面调查.
- 沙普利增量解释 (SHAP) 和整体方法 (随机森林,XGBoost) 用于特征选择.
- 机器学习模型 (逻辑回归,AdaBoost,BPNN) 进行了训练和验证.
主要成果:
- 六个关键特征显著预测了心理健康结果:惩罚,适应问题,情绪自我调节,学习压力,社会支持利用和情绪识别.
- SHAP+BPNN模型在内部验证 (C指数=0.9120) 和外部验证 (C指数=0.9749) 中实现了高性能.
结论:
- 机器学习模型,特别是SHAP+BPNN,在预测年轻人的心理健康方面显示出很强的临床应用潜力.
- 研究结果强调了由临床预测模型为基础的有针对性的干预措施的有效性.
- 限制包括自我报告的数据,样本代表性,以及少数群体潜在的算法偏见.
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