Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires
Thong Nguyen, Andrew Yates, Ayah Zirikly, Bart Desmet, Arman Cohan
Abstract
Automated methods have been widely used to identify and analyze mental health conditions (e.g., depression) from various sources of information, including social media. Yet, deployment of such models in real-world healthcare applications faces challenges including poor out-of-domain generalization and lack of trust in black box models. In this work, we propose approaches for depression detection that are constrained to different degrees by the presence of symptoms described in PHQ9, a questionnaire used by clinicians in the depression screening process. In dataset-transfer experiments on three social media datasets, we find that grounding the model in PHQ9’s symptoms substantially improves its ability to generalize to out-of-distribution data compared to a standard BERT-based approach. Furthermore, this approach can still perform competitively on in-domain data. These results and our qualitative analyses suggest that grounding model predictions in clinically-relevant symptoms can improve generalizability while producing a model that is easier to inspect.- Anthology ID:
- 2022.acl-long.578
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Smaranda Muresan, Preslav Nakov, Aline Villavicencio
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 8446–8459
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.578
- DOI:
- 10.18653/v1/2022.acl-long.578
- Cite (ACL):
- Thong Nguyen, Andrew Yates, Ayah Zirikly, Bart Desmet, and Arman Cohan. 2022. Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 8446–8459, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Improving the Generalizability of Depression Detection by Leveraging Clinical Questionnaires (Nguyen et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.acl-long.578.pdf
- Code
- thongnt99/acl22-depression-phq9
- Data
- SMHD