Abstract
Existing Mental Disease Detection (MDD) research largely studies the detection of a single disorder, overlooking the fact that mental diseases might occur in tandem. Many approaches are not backed by domain knowledge (e.g., psychiatric symptoms) and thus fail to produce interpretable results. To tackle these issues, we propose an MDD framework that is capable of learning the shared clues of all diseases, while also capturing the specificity of each single disease. The two-stream architecture which simultaneously processes text and symptom features can combine the strength of both modalities and offer knowledge-based explainability. Experiments on the detection of 7 diseases show that our model can boost detection performance by more than 10%, especially in relatively rare classes.- Anthology ID:
- 2023.emnlp-main.562
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9071–9084
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.562
- DOI:
- 10.18653/v1/2023.emnlp-main.562
- Cite (ACL):
- Siyuan Chen, Zhiling Zhang, Mengyue Wu, and Kenny Zhu. 2023. Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 9071–9084, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Detection of Multiple Mental Disorders from Social Media with Two-Stream Psychiatric Experts (Chen et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/naacl24-info/2023.emnlp-main.562.pdf