We describe the development of a model to detect user-level clinical depression based on a user's temporal social media posts. Our model uses a Depression Symptoms Detection (DSD) classifier, which is trained on the largest existing samples of clinician annotated tweets for clinical depression symptoms. We subsequently use our DSD model to extract clinically relevant features, e.g., depression scores and their consequent temporal patterns, as well as user posting activity patterns, e.g., quantifying their ``no activity'' or ``silence.'' Furthermore, to evaluate the efficacy of these extracted features, we create three kinds of datasets including a test dataset, from two existing well-known benchmark datasets for user-level depression detection. We then provide accuracy measures based on single features, baseline features and feature ablation tests, at several different levels of temporal granularity. The relevant data distributions and clinical depression detection related settings can be exploited to draw a complete picture of the impact of different features across our created datasets. Finally, we show that, in general, only semantic oriented representation models perform well. However, clinical features may enhance overall performance provided that the training and testing distribution is similar, and there is more data in a user's timeline. The consequence is that the predictive capability of depression scores increase significantly while used in a more sensitive clinical depression detection settings.
翻译:我们描述了一种基于用户的时间序列社交媒体帖子来检测用户级别临床抑郁症的模型。我们的模型使用一个抑郁症状检测(DSD)分类器,该分类器基于临床医生标注的用于抑郁症状的推文的最大现有样本进行训练。我们随后使用我们的 DSD 模型提取临床相关特征,例如抑郁症分数及其随后的时间模式,以及用户发布活动模式,例如量化他们的“无活动”或“沉默”。此外,为了评估这些提取的特征的有效性,我们创建了三种数据集,包括来自两个现有的用户级别抑郁症检测基准数据集的测试数据集。我们随后基于单一特征、基线特征和特征消融测试,在几个不同级别的时间粒度上提供准确性测量。相关数据分布和临床抑郁症检测相关设置可以用来绘制一个完整的不同特征在我们创建的数据集上的影响图。最后,我们表明,总体而言,只有语义相关的表示模型表现良好。然而,如果训练和测试分布相似,并且在用户的时间轴上有更多数据,临床特征可能增强整体性能。其后果是,在更敏感的临床抑郁症检测设置中使用抑郁症分数的预测能力显著提高。