一个基于参与者的情绪调节 (ML-SAD) 的机器学习网络应用程序,用于选基于参与者的情绪调节 (ML-SAD) 的社会焦虑障碍
Sara Ahmadi Majd1,2, Mohamad Rasoul Parsaeian2, Mohsen Madani3
1Campus Institute Data Science, Georg-August-Universität Göttingen, Göttingen, Germany.
Frontiers in robotics and AI
|October 13, 2025
概括
社会焦虑障碍 (SAD) 经常被忽视. 一个新的机器学习网络应用程序有效地选年轻成年人的SAD,识别那些需要进一步支持的人,准确度超过80%.
科学领域:
- 心理学 心理学 心理学
- 计算机科学 计算机科学
- 数字健康数字健康
背景情况:
- 社会焦虑障碍 (SAD) 诊断和治疗不足,需要可访问的查工具.
- 早期识别SAD对于及时干预和改善患者结果至关重要.
研究的目的:
- 开发和评估基于机器学习的网络应用程序,用于对社会焦虑障碍进行自我查.
- 评估该工具在识别波斯语人口中患有SAD的个人的有效性.
主要方法:
- 开发了一个带有10个多媒体场景的网络应用程序,评估能力和情绪调节策略.
- 招募了488名年轻成年人 (18-35岁),并将他们分为SAD和非SAD组.
- 训练和评估多个机器学习模型来预测SAD状态.
主要成果:
- 机器学习模型在识别患有社会焦虑障碍的个人的准确率超过80%.
- 开发的网络应用程序显示出作为有效的SAD查工具的巨大潜力.
- 该研究强调了技术在心理健康评估中的实用性.
结论:
- 基于机器学习的Web应用程序是自我查社会焦虑障碍的一个有希望的工具.
- 该工具可以帮助识别需要进一步临床评估和支持的个体.
- 广泛使用的数字工具可以改善获得心理健康查和资源的机会.
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