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Depression is a prominent health challenge to the world, and early riskdetection (ERD) of depression from online posts can be a promising techniquefor combating the threat. Early depression detection faces the challenge ofefficiently tackling streaming data, balancing the tradeoff between timeliness,accuracy and explainability. To tackle these challenges, we propose apsychiatric scale guided risky post screening method that can capture riskyposts related to the dimensions defined in clinical depression scales, andproviding interpretable diagnostic basis. A Hierarchical Attentional Networkequipped with BERT (HAN-BERT) is proposed to further advance explainablepredictions. For ERD, we propose an online algorithm based on an evolving queueof risky posts that can significantly reduce the number of model inferences toboost efficiency. Experiments show that our method outperforms the competitivefeature-based and neural models under conventional depression detectionsettings, and achieves simultaneous improvement in both efficacy and efficiencyfor ERD.

Psychiatric Scale Guided Risky Post Screening for Early Detection of Depression