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
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
Bibkey:
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)
Copy Citation:
PDF:
https://preview.aclanthology.org/ingestion-script-update/2022.acl-long.578.pdf
Video:
 https://preview.aclanthology.org/ingestion-script-update/2022.acl-long.578.mp4
Code
 thongnt99/acl22-depression-phq9
Data
SMHD