通过机器学习预测内化症状:识别需要护理的个体
Mengxing Wang1, Lauren L Richmond1, Jessica L Schleider1
1Department of Psychology, Stony Brook University, Stony Brook, New York, USA.
Journal of American college health : J of ACH
|November 9, 2023
概括
机器学习使用日常干扰数据准确预测心理健康需求. 这种方法有助于识别需要紧急心理支持的学生,教师和工作人员.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 公共卫生 公共卫生
背景情况:
- 心理健康挑战在大学环境中很普遍.
- 识别需要及时心理健康护理的个体至关重要.
- 现有的方法可能无法完全捕捉影响心理健康的日常生活中断的细微差别.
研究的目的:
- 开发和验证一种机器学习模型,用于识别需要紧急心理健康护理的个人.
- 将随机森林算法的有效性与传统的回归分析进行比较.
- 确定特定的日常生活中断,预测内化症状.
主要方法:
- 招募了2,409名大学生,教师和工作人员.
- 使用COVID-19影响调查,患者健康问卷-9 (PHQ-9) 和通用焦虑障碍-7 (GAD-7) 收集数据.
- 训练并验证了一个随机森林模型,以根据调查组合结果预测PHQ-9和GAD-7分数.
主要成果:
- 随机森林模型实现了准确的前预测,平均R平方值为0.429.
- 确定了与日常生活中断相关的关键变量,这些变量可以预测心理健康症状.
- 证明了模型对概括和可靠预测的能力.
结论:
- 预测模型,特别是随机森林,在心理健康护理中显示出临床实用性.
- 日常生活中断是识别有内化症状的个体的重要指标.
- 这种方法为学术界积极支持心理健康提供了一种新方法.
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