Analyzing Symptom-based Depression Level Estimation through the Prism of Psychiatric Expertise

Navneet Agarwal, Kirill Milintsevich, Lucie Metivier, Maud Rotharmel, Gaël Dias, Sonia Dollfus

Abstract
The ever-growing number of people suffering from mental distress has motivated significant research initiatives towards automated depression estimation. Despite the multidisciplinary nature of the task, very few of these approaches include medical professionals in their research process, thus ignoring a vital source of domain knowledge. In this paper, we propose to bring the domain experts back into the loop and incorporate their knowledge within the gold-standard DAIC-WOZ dataset. In particular, we define a novel transformer-based architecture and analyse its performance in light of our expert annotations. Overall findings demonstrate a strong correlation between the psychological tendencies of medical professionals and the behavior of the proposed model, which additionally provides new state-of-the-art results.
Anthology ID:
2024.lrec-main.87
Volume:
Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
Month:
May
Year:
2024
Address:
Torino, Italia
Editors:
Nicoletta Calzolari, Min-Yen Kan, Veronique Hoste, Alessandro Lenci, Sakriani Sakti, Nianwen Xue
Venues:
LREC | COLING
SIG:
Publisher:
ELRA and ICCL
Note:
Pages:
974–983
Language:
URL:
https://aclanthology.org/2024.lrec-main.87
DOI:
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Cite (ACL):
Navneet Agarwal, Kirill Milintsevich, Lucie Metivier, Maud Rotharmel, Gaël Dias, and Sonia Dollfus. 2024. Analyzing Symptom-based Depression Level Estimation through the Prism of Psychiatric Expertise. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 974–983, Torino, Italia. ELRA and ICCL.
Cite (Informal):
Analyzing Symptom-based Depression Level Estimation through the Prism of Psychiatric Expertise (Agarwal et al., LREC-COLING 2024)
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