机器学习方法来检测焦虑:趋势,模型评估,以及未来的方向
Meruyert Taskynbayeva1, Alina Gutoreva1
1Kazakh-British Technical University, Almaty, Kazakhstan.
Frontiers in artificial intelligence
|November 6, 2025
概括
机器学习 (ML) 有效地预测焦虑,像随机森林这样的技术显示出高准确度. 整合可解释的ML和多样化的数据对于现实应用和早期干预至关重要.
科学领域:
- 心理健康研究 心理健康研究
- 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:
- 焦虑是一种广泛的心理健康问题,特别影响数字环境中的年轻人.
- 越来越需要先进的工具来预测和减轻焦虑.
- 机器学习 (ML) 为焦虑预测和个性化干预提供了希望.
研究的目的:
- 系统地审查在焦虑预测中的ML应用.
- 评估焦虑预测研究中使用的算法,特征和验证技术.
- 确定临床部署的趋势,差距和影响.
主要方法:
- 在Scopus和谷歌学者 (2018-2025) 的系统文献搜索.
- 包括使用ML进行焦虑预测的研究与临床或行为数据.
- 基于关键词的存在和对准与通过ML的焦虑预测的选.
主要成果:
- 纳入了19项研究,共有44,608名参与者.
- 随机森林和梯度提升实现了高预测准确度 (高达98%).
- GAD-7和DASS-21是常用的工具;F1得分,AUC和特异性是关键指标.
结论:
- ML对于焦虑的检测和早期干预是有效的.
- 可解释的ML (XAI) 和多样化,验证的数据集对于临床实用性至关重要.
- 需要更多的研究来预测症状出现之前的焦虑.
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