在COVID-19期间加拿大医疗保健工作者心理健康预测模型
Bhawna Kumari1, Nidhi Goyal1, Christo El Morr2
1Indian Institute of Technology, Kharagpur, WB, India.
预测模型准确地预测医疗保健工作者的心理健康和压力. 像XGBoost和Random Forest这样的机器学习算法识别了关键预测因素,从而实现了主动的工作场所心理健康支持.
科学领域:
- 公共卫生 公共卫生
- 心理健康研究 心理健康研究
- 医疗信息学 医疗信息学
背景情况:
- COVID-19 疫情对全球心理健康产生了重大影响.
- 了解和预测医疗保健工作者之间的心理健康挑战对于应急准备至关重要.
研究的目的:
- 开发和评估医疗保健工作者对心理健康和生活压力的预测模型.
- 确定影响该人口心理健康的关键因素.
主要方法:
- 对18139名加拿大医疗保健工作者进行了横截面调查.
- 八个机器学习算法 (XGBoost,随机森林等) 用于构建预测模型.
- 用AUC分数,准确度和精度来评估模型性能.
主要成果:
- XGBoost在预测感知精神健康方面获得了最高的AUC (82.07%).
- 随机森林在预测感知生活压力方面表现最好 (AUC 77.74%).
- 关键预测因素包括感知健康,年龄组和大流行前的心理健康状况.
结论:
- 开发的模型显示了医疗保健工作者的心理健康和压力的高预测准确度.
- 可扩展,具有成本效益的虚拟心理健康解决方案可以减轻与工作场所相关的心理健康问题.
- 积极的战略对于支持医疗保健工作者的福祉至关重要.
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