在公共卫生危机期间,通过对卫生保健工作者进行预测建模来平衡心理健康
Jiana Wang1, Lin Feng2, Nana Meng2
1School of Public Health, Health Science Center, Ningbo University, No.818 Fenghua Road, Ningbo, 315211, Zhejiang, People's Republic of China.
在公共卫生紧急情况中,前线医疗保健工作者面临高风险的抑郁和焦虑. 机器学习模型确定了诸如倦怠和工作负载等关键因素,建议针对性干预来支持HCW.
科学领域:
- 公共卫生 公共卫生
- 心理健康 心理健康
- 机器学习 机器学习
背景情况:
- 在COVID-19等公共卫生紧急情况期间,医疗保健工作者 (HCW) 患抑郁和焦虑的风险增加.
- 关于危机期间HCW心理健康的预测模型的研究有限.
研究的目的:
- 开发和验证机器学习模型,用于预测HCW中的抑郁和焦虑.
- 确定影响HCW心理健康的关键个人,人际和机构因素.
主要方法:
- 一个随机森林分类器 (RFC) 用于预测349个HCW的抑郁症 (PHQ-9) 和焦虑症 (GAD-7).
- 合成少数人过量采样技术 (SMOTE) 解决了数据不平衡问题.
- SHAP值评估了预测因素的贡献.
主要成果:
- 抑郁症和焦虑症的患病率分别为28.37%和33.52%.
- 模型的AUC为0.88的抑郁症和0.72的焦虑症.
- 关键预测因素包括倦怠,性,情绪劳动,适应能力和工作时间.
结论:
- 通过SMOTE增强的机器学习模型可以预测HCW抑郁和焦虑.
- 干预措施应侧重于减轻倦怠,增强性和适应能力,以及管理工作量.
- 积极主动和紧急后的支持对HCW心理健康至关重要.
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