论文标题
精神病量表指导有风险的筛查，以提早发现抑郁症
Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression
论文作者
论文摘要
抑郁症是对世界的重大健康挑战，在线帖子中对抑郁症的早期风险检测（ERD）可能是打击威胁的一种有前途的技术。早期抑郁症检测面临有效解决流数据，平衡及时性，准确性和解释性之间的权衡的挑战。为了应对这些挑战，我们提出了一种精神病量表指导的风险筛查方法，该方法可以捕获与临床抑郁量表中定义的维度相关的风险帖子，并提供可解释的诊断基础。提出了配备BERT（Han-Bert）的分层注意网络，以进一步推进可解释的预测。对于ERD，我们提出了一种基于危险帖子不断发展的队列的在线算法，可以大大减少模型推断的数量以提高效率。实验表明，我们的方法在常规抑郁检测设置下优于基于竞争特征的神经模型，并同时提高了ERD的功效和效率。
Depression is a prominent health challenge to the world, and early risk detection (ERD) of depression from online posts can be a promising technique for combating the threat. Early depression detection faces the challenge of efficiently tackling streaming data, balancing the tradeoff between timeliness, accuracy and explainability. To tackle these challenges, we propose a psychiatric scale guided risky post screening method that can capture risky posts related to the dimensions defined in clinical depression scales, and providing interpretable diagnostic basis. A Hierarchical Attentional Network equipped with BERT (HAN-BERT) is proposed to further advance explainable predictions. For ERD, we propose an online algorithm based on an evolving queue of risky posts that can significantly reduce the number of model inferences to boost efficiency. Experiments show that our method outperforms the competitive feature-based and neural models under conventional depression detection settings, and achieves simultaneous improvement in both efficacy and efficiency for ERD.
