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青少年心理健康的数字表型:利用机器学习进行可行性研究,从主动和被动智能手机数据中预测心理健康风险.

Balasundaram Kadirvelu1, Teresa Bellido Bel2, Aglaia Freccero2

  • 1Brain & Behaviour Lab, Department of Computing and Department of Bioengineering, Imperial College London, Royal School of Mines, London, SW72AZ, United Kingdom, 44 20 7594 6373.

Journal of medical Internet research
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PubMed
概括

智能手机数字表型有效地通过整合主动和被动数据来预测青少年心理健康风险. 这种可扩展的方法有助于在非临床年轻人中早期检测,弥合了社区预防的关键差距.

关键词:
欧洲药品监督管理局 (EMA) 是一个.人工智能的人工智能是人工智能.数字健康数字健康早期干预是早期的干预.生态瞬间评估 环境瞬间评估移动健康 移动健康 移动健康 移动健康移动应用程序 移动应用程序移动健康的移动健康智能手机传感感应的感应青少年心理健康 青少年心理健康

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科学领域:

  • 数字健康数字健康
  • 机器学习在心理健康中的应用
  • 青少年心理学 青少年心理学

背景情况:

  • 青少年非常容易患上精神障碍,通常在25岁之前就会出现精神障碍.
  • 大多数患有心理健康症状的青少年不会寻求专业的支持.
  • 数字表型化为青少年早期风险检测提供了一种低负担的方法.

研究的目的:

  • 评估智能手机应用程序用于预测非临床青少年心理健康风险的可行性.
  • 使用机器学习集成主动 (自我报告) 和被动 (传感器) 数据.
  • 识别内部化/外部化困难,饮食障碍,失眠和自杀念头的风险.

主要方法:

  • 103名青少年使用了Mindcraft应用程序14天,每天收集自我报告和被动传感器数据.
  • 开发了一种具有对比预训练的深度学习模型,用于对心理健康结果的二元分类.
  • 使用离开一个主体的交叉验证来评估性能,并与其他ML模型进行比较.

主要成果:

  • 综合的主动和被动数据在心理健康结果中实现了0.67-0.77的平衡准确度.
  • 对比式学习方法提高了模型稳定性和预测稳定性.
  • 沙普利添加式解释 (SHAP) 确定了临床相关的特征,证实了数据集成的价值.

结论:

  • 基于智能手机的数字表型是可行的,并且对于预测非临床青少年的心理健康风险是有用的.
  • 这种综合数据方法对早期检测和可扩展的干预非常有希望.
  • 这些发现支持了青少年心理健康的社区预防工作.