应对危机的计划:在COVID-19期间使用机器学习预测人口中的焦虑
Bhawna Kumari1, Nidhi Goyal1, Christo Elmorr2
1Indian Institute of Technology, Kharagpur, India.
Studies in health technology and informatics
|October 23, 2023
概括
机器学习模型可以使用调查数据预测加拿大人的焦虑症状. 轻GBM模型实现了87.45%的AUC,将感知到的心理健康和压力确定为关键焦虑预测因素.
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:
- 随着COVID-19的蔓延,全球人口的心理健康受到严重影响.
- 了解焦虑患病率和预测因素对于公共卫生干预至关重要.
- 加拿大统计局对加拿大人进行了调查,以评估大流行期间的心理健康挑战.
研究的目的:
- 开发和评估用于预测焦虑症状的机器学习模型.
- 确定导致加拿大成年人焦虑的关键因素.
- 在健康危机期间为虚拟护理策略的开发提供信息.
主要方法:
- 使用了45989名加拿大调查受访者的数据集.
- 应用并比较了八种机器学习算法:物流回归,随机森林,天真贝叶斯,K近邻,自适应提升,多层感知器,XGBoost和LightGBM.
- 使用曲线下面面积 (AUC) 评分评估模型性能.
主要成果:
- 轻GBM表现出最高的预测性能,其AUC为87.45%.
- 预测焦虑的最有影响力的特征是"与身体距离之前相比,对心理健康的感知"",感知生活压力"和"感知心理健康".
- 一个值得注意的局限性是样本对加拿大更广泛的人口的代表性.
结论:
- 机器学习模型可以有效地预测加拿大人口调查中的焦虑症状.
- 与心理健康和生活压力相关的感知因素是焦虑的重要预测因素.
- 研究结果强调,在危机期间规划虚拟心理健康护理时需要考虑这些因素.
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