论文标题
从人格搜索和YouTube历史记录中检测患有抑郁症的人
Detecting Individuals with Depressive Disorder fromPersonal Google Search and YouTube History Logs
论文作者
论文摘要
抑郁症是全球人群中最普遍的精神疾病之一。但是，传统的筛查方法需要进行严格的面试访谈，并且可能无法立即提供干预措施。在这项工作中，我们利用无处不在的个人纵向Google搜索和YouTube参与日志来检测患有抑郁症的人。我们从212美元的参与者那里收集了Google搜索和YouTube历史数据以及临床抑郁评估结果（其中99美元的损失中等至重度）。然后，我们提出了一个个性化框架，以基于相互兴奋的点过程对患有和没有抑郁症状的人进行分类，以捕获在线活动的时间和语义方面。我们的最佳模型的平均F1分数为$ 0.77 \ pm 0.04 $，AUC ROC为$ 0.81 \ pm 0.02 $。
Depressive disorder is one of the most prevalent mental illnesses among the global population. However, traditional screening methods require exacting in-person interviews and may fail to provide immediate interventions. In this work, we leverage ubiquitous personal longitudinal Google Search and YouTube engagement logs to detect individuals with depressive disorder. We collected Google Search and YouTube history data and clinical depression evaluation results from $212$ participants ($99$ of them suffered from moderate to severe depressions). We then propose a personalized framework for classifying individuals with and without depression symptoms based on mutual-exciting point process that captures both the temporal and semantic aspects of online activities. Our best model achieved an average F1 score of $0.77 \pm 0.04$ and an AUC ROC of $0.81 \pm 0.02$.
