@inproceedings{kang-etal-2024-cure,
title = "{CURE}: Context- and Uncertainty-Aware Mental Disorder Detection",
author = "Kang, Migyeong and
Choi, Goun and
Jeon, Hyolim and
An, Ji Hyun and
Choi, Daejin and
Han, Jinyoung",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.994",
pages = "17924--17940",
abstract = "As the explainability of mental disorder detection models has become important, symptom-based methods that predict disorders from identified symptoms have been widely utilized. However, since these approaches focused on the presence of symptoms, the context of symptoms can be often ignored, leading to missing important contextual information related to detecting mental disorders. Furthermore, the result of disorder detection can be vulnerable to errors that may occur in identifying symptoms. To address these issues, we propose a novel framework that detects mental disorders by leveraging symptoms and their context while mitigating potential errors in symptom identification. In this way, we propose to use large language models to effectively extract contextual information and introduce an uncertainty-aware decision fusion network that combines predictions of multiple models based on quantified uncertainty values. To evaluate the proposed method, we constructed a new Korean mental health dataset annotated by experts, named KoMOS. Experimental results demonstrate that the proposed model accurately detects mental disorders even in situations where symptom information is incomplete.",
}
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<abstract>As the explainability of mental disorder detection models has become important, symptom-based methods that predict disorders from identified symptoms have been widely utilized. However, since these approaches focused on the presence of symptoms, the context of symptoms can be often ignored, leading to missing important contextual information related to detecting mental disorders. Furthermore, the result of disorder detection can be vulnerable to errors that may occur in identifying symptoms. To address these issues, we propose a novel framework that detects mental disorders by leveraging symptoms and their context while mitigating potential errors in symptom identification. In this way, we propose to use large language models to effectively extract contextual information and introduce an uncertainty-aware decision fusion network that combines predictions of multiple models based on quantified uncertainty values. To evaluate the proposed method, we constructed a new Korean mental health dataset annotated by experts, named KoMOS. Experimental results demonstrate that the proposed model accurately detects mental disorders even in situations where symptom information is incomplete.</abstract>
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%0 Conference Proceedings
%T CURE: Context- and Uncertainty-Aware Mental Disorder Detection
%A Kang, Migyeong
%A Choi, Goun
%A Jeon, Hyolim
%A An, Ji Hyun
%A Choi, Daejin
%A Han, Jinyoung
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F kang-etal-2024-cure
%X As the explainability of mental disorder detection models has become important, symptom-based methods that predict disorders from identified symptoms have been widely utilized. However, since these approaches focused on the presence of symptoms, the context of symptoms can be often ignored, leading to missing important contextual information related to detecting mental disorders. Furthermore, the result of disorder detection can be vulnerable to errors that may occur in identifying symptoms. To address these issues, we propose a novel framework that detects mental disorders by leveraging symptoms and their context while mitigating potential errors in symptom identification. In this way, we propose to use large language models to effectively extract contextual information and introduce an uncertainty-aware decision fusion network that combines predictions of multiple models based on quantified uncertainty values. To evaluate the proposed method, we constructed a new Korean mental health dataset annotated by experts, named KoMOS. Experimental results demonstrate that the proposed model accurately detects mental disorders even in situations where symptom information is incomplete.
%U https://aclanthology.org/2024.emnlp-main.994
%P 17924-17940
Markdown (Informal)
[CURE: Context- and Uncertainty-Aware Mental Disorder Detection](https://aclanthology.org/2024.emnlp-main.994) (Kang et al., EMNLP 2024)
ACL
- Migyeong Kang, Goun Choi, Hyolim Jeon, Ji Hyun An, Daejin Choi, and Jinyoung Han. 2024. CURE: Context- and Uncertainty-Aware Mental Disorder Detection. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 17924–17940, Miami, Florida, USA. Association for Computational Linguistics.