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.

本文提出了一种基于心理量表指导的风险帖子筛选方法，采用具有可解释性的 Hierarchical Attentional Network 和 BERT 模型进行预测，以及一种基于不断更新的风险帖子队列的在线算法，用于快速高效地检测抑郁风险。实验证明，该方法在传统抑郁预测中胜过竞争对手的基于特征和神经网络的模型，并在 ERD 的效率和准确性方面实现了同时改进。

精神医学量表辅助评估筛查高危人群以早期检测抑郁症