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.
BibTeX
@inproceedings{agarwal-etal-2024-analyzing,
title = "Analyzing Symptom-based Depression Level Estimation through the Prism of Psychiatric Expertise",
author = {Agarwal, Navneet and
Milintsevich, Kirill and
Metivier, Lucie and
Rotharmel, Maud and
Dias, Ga{\"e}l and
Dollfus, Sonia},
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.87/",
pages = "974--983"
}