通过机器学习揭示COVID-19对心理健康的影响
Salah Bouktif1, Akib Mohi Ud Din Khanday2, Ali Ouni3
1Department of Computer Science and Software Engineering, United Arab Emirates University, Al Ain, Abu Dhabi, 15551, United Arab Emirates.
JAMIA open
|February 23, 2026
概括
在COVID-19期间,大流行前的抑郁症模型失败了. 使用流行病数据的新模型,如随机森林,实现了98.1%的准确性. 关键因素包括延迟护理和贫困,为未来的心理健康准备提供了信息.
科学领域:
- 公共卫生 公共卫生
- 计算精神病学是一种计算精神病学.
- 流行病学 流行病学
背景情况:
- COVID-19 疫情对全球心理健康产生了重大影响,需要了解其对心理健康的影响.
- 预先存在的抑郁症预测模型可能不会准确地反映全球危机期间的心理健康状况.
研究的目的:
- 在COVID-19时代数据上评估大流行前抑郁症预测模型的性能.
- 利用机器学习 (ML) 和可解释的人工智能 (XAI) 开发和验证新的模型,用于在疫情期间预测抑郁症.
- 在疫情期间识别影响抑郁症的关键因素,以改善心理健康干预和未来的疫情准备.
主要方法:
- 利用了2019-2022年的国家健康访谈调查 (NHIS) 数据.
- 采用ML技术,包括特征选择和XAI,分析心理健康的社会决定因素.
- 使用超过10万个数据点进行训练和评估模型,火车测试比例为80%-20%.
主要成果:
- 在COVID-19前数据上训练的分类器在COVID-19时代数据上表现不佳.
- 在流行病特定数据上重新训练的模型实现了高准确性,随机森林 (RF) 达到98.10%.
- XAI认为延迟医疗,家庭贫困和减少社交活动是影响抑郁症的重要因素.
结论:
- 流行病前的抑郁模型对像COVID-19流行病这样的重大全球事件没有弹性.
- 机器学习模型,特别是RF,在危机特定数据上进行重新训练,对于在流行病期间准确预测心理健康至关重要.
- 了解关键的社会经济和行为因素对于在公共卫生危机期间开发有针对性的心理健康支持至关重要.
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