Abstract
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.
Keywords
- Natural Language Processing: Text Classification
- Machine Learning: Time-series; Data Streams
- AI Ethics, Trust, Fairness: Explainability and Interpretability
- Humans and AI: Computational Sustainability and Human Well-Being
- Machine Learning: Attention Models
- Machine Learning: Classification
- Machine Learning: Sequence and Graph Learning
- AI Ethics, Trust, Fairness: Ethical, Legal and Societal Issues
- Multidisciplinary Topics and Applications: Health and Medicine
- Multidisciplinary Topics and Applications: Sustainable Development Goals
- Multidisciplinary Topics and Applications: Web and Social Networks
- Multidisciplinary Topics and Applications: General
- Natural Language Processing: General
