使用随机森林机器学习方法预测接受传染病治疗的患者与焦虑相关的因素
Ruijie Chang1,2,3, Chenrui Li1,2,3, Dake Shi4
1Public Health Research Center, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, No. 1111, Xianxia Road, Shanghai, 200336, China.
在传染病患者中,焦虑是很常见的,尤其是在COVID-19期间. 关键的危险因素包括陷入困境的感觉,耻辱和丧, 而社会支持则是保护性的. 机器学习识别了这些预测因素,
科学领域:
- 传染性疾病
- 心理健康
- 机器学习
背景情况:
- 患有传染病的患者面临更高的焦虑风险.
- COVID-19 疫情加剧了感染者的焦虑和抑郁.
- 迫切需要对传染病中精神健康问题的风险因素进行调查.
研究的目的:
- 研究感染性疾病患者与焦虑相关的危险因素.
- 使用机器学习开发焦虑风险的预测模型.
- 确定个性化焦虑干预的关键预测因素.
主要方法:
- 一项涉及2022年上海1283名患者的横截面研究.
- 使用随机森林分类器与Boruta算法进行预测选.
- 根据已识别的风险因素构建了一个名图.
主要成果:
- 陷入困境,失败和耻辱感与焦虑有积极的关联 (P < 0. 01).
- 社会支持与焦虑有负相关性 (P < 0. 001).
- 该预测模型的C指数为0. 858和AUC为0. 861.
结论:
- 一种基于机器学习的模型有效预测了COVID-19患者的焦虑风险.
- 确定了关键的风险因素:陷入困境,失败,耻辱和社会支持.
- 提供临床实践,公共卫生和个性化心理健康干预的科学基础.
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