机器学习用于焦虑和抑郁的分析和风险评估在紧急情况发生后
Guillermo Villanueva Benito1, Ximena Goldberg2, Nicolai Brachowicz2
1Barcelona Institute for Global Health (ISGlobal), C/ del Dr. Aiguader, 88, Barcelona 08003, Catalonia, Spain; Universitat Pompeu Fabra (UPF), Spain.
Artificial intelligence in medicine
|October 9, 2024
概括
机器学习工具可以识别有抑郁,焦虑和压力风险的个人,帮助公共卫生应对COVID-19等危机. 这种方法有助于为有针对性的心理健康干预分层人口.
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.
- 医疗保健中的人工智能
背景情况:
- 心理健康障碍是一个日益严重的公共卫生问题,COVID-19大流行加剧了这一问题.
- 疫情暴露了准备不足,突出了早期识别受风险人群和定制干预的必要性.
- 开发强大的风险评估工具对于积极的心理健康管理至关重要.
研究的目的:
- 开发一种基于机器学习 (ML) 的风险评估工具,用于焦虑,抑郁和自我感知压力.
- 利用可解释的AI (人工智能) 方法来识别关键的风险因素.
- 为了针对性干预,将人口分为不同的风险概况.
主要方法:
- 分析了一组来自西班牙北部的9291名个人,这些人有COVID-19后的心理健康数据.
- 使用ML分类算法预测抑郁,焦虑和自我报告的压力水平 (健康,轻度,严重).
- 为了模型解释和风险聚类,使用了SHAP (夏普利添加式扩展) 和UMAP (统一多重近似和投影) 的组合.
主要成果:
- ML模型实现了良好的预测性能,AUROC得分为0.77的抑郁症,0.72的焦虑症和0.73的自我感知压力.
- 确定的关键风险因素包括自我报告的健康状况不佳,先前存在的慢性精神健康状况和社会支持不足.
- 确定了特定的高风险概况,例如女性睡眠时间减少,特别是自我感知的压力.
结论:
- SHAP和UMAP的整合为风险分层和发展有针对性的心理健康干预提供了有价值的见解.
- 这种数据驱动的方法提高了对公共卫生危机的心理健康准备.
- 建议在现实环境中进一步验证,以优化公共卫生政策并解决危机对心理健康的影响.
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