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抑郁症 通过触摸屏远程检测 打字行为

Ruba Fadul, Hessa Alfalahi, Aamna Al Shehhi

    Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
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    此摘要是机器生成的。

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    科学领域:

    • 数字生物标志物 数字生物标志物
    • 心理健康信息学心理健康信息学
    • 机器学习在医疗保健中的应用

    背景情况:

    • 抑郁症 (DD) 是全球主要的健康问题,也是导致残疾的原因.
    • 需要客观和被动查工具来早期检测和管理DD.
    • 个人动态表达,包括智能手机使用模式,提供了对心理状态的见解.

    研究的目的:

    • 调查触摸屏打字中的键盘键盘动态对于检测抑郁症障碍的有用性.
    • 评估基于数字生物标志物的深度学习模型来识别抑郁倾向.
    • 为了比较卷积神经网络 (CNN),长短期记忆 (LSTM) 和CNN-LSTM模型的性能.

    主要方法:

    • 从10名DD患者和14名健康对照 (HC) 收集了23,264次打字会话.
    • 在常规智能手机交互过程中,利用了不显眼地捕获的按键序列.
    • 应用并比较CNN,LSTM和CNN-LSTM模型,使用两个特征组合和LOSO交叉验证.

    主要成果:

    • 通过LSTM-with-hold-time (LSTM-HT) 模型实现了最高的性能.
    • 最好的模型得到了0.86的曲线下的面积 (AUC).
    • 报告了高灵敏度 (0.8) 和特异性 (0.93),表明了强大的诊断能力.

    结论:

    • 按键动态作为一个可行的数字生物标志物,用于选抑郁症.
    • 深度学习方法,特别是LSTM,可以有效地从打字模式中检测抑郁倾向.
    • 调查结果支持开发客观的数字工具,用于现实世界的心理健康查和监测.