论文标题
使用卷积变异自动编码器来预测动作法数据的创伤后健康结果
Using Convolutional Variational Autoencoders to Predict Post-Trauma Health Outcomes from Actigraphy Data
论文作者
论文摘要
抑郁症和创伤后应激障碍（PTSD）是通常与经历创伤事件有关的精神病。通过非侵入性技术（例如基于活动的算法）估算心理健康状况可以帮助确定成功的早期干预措施。在这项工作中，我们使用了从1113个人中捕获的运动级智能手表后的运动活动。卷积变分自动编码器（VAE）架构用于从四个星期的行动术数据中进行无监督的特征提取。通过使用VAE潜在变量和参与者的创伤前身体健康状况作为特征，Logistic回归分类器在接收器操作特征曲线（AUC）下达到了0.64的区域，以估计心理健康结果。结果表明，在长期研究中，VAE模型是对心理健康结果进行精神健康结果的有前途的方法。
Depression and post-traumatic stress disorder (PTSD) are psychiatric conditions commonly associated with experiencing a traumatic event. Estimating mental health status through non-invasive techniques such as activity-based algorithms can help to identify successful early interventions. In this work, we used locomotor activity captured from 1113 individuals who wore a research grade smartwatch post-trauma. A convolutional variational autoencoder (VAE) architecture was used for unsupervised feature extraction from four weeks of actigraphy data. By using VAE latent variables and the participant's pre-trauma physical health status as features, a logistic regression classifier achieved an area under the receiver operating characteristic curve (AUC) of 0.64 to estimate mental health outcomes. The results indicate that the VAE model is a promising approach for actigraphy data analysis for mental health outcomes in long-term studies.
