TY - GEN
T1 - Enhancing Depression Detection from Narrative Interviews Using Language Models
AU - Sood, Palak
AU - Yang, Xinming
AU - Wang, Ping
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In recent years, the prevalence of mental health issues among young people has significantly increased, especially due to the consequences of COVID-19 pandemic and the widespread adoption of remote work arrangements. Depression has emerged as a major global mental health concern due to its potentially devastating consequences. However, existing methods for depression detection still face several challenges, such as limited data availability, imbalanced labels, and inadequate consideration of contextual information. To tackle these challenges, in this paper, we first create a larger dataset, namely I-DAIC, for depression detection by integrating three existing datasets in the literature. We further fine-tune and examine two pre-trained transformer-based language models by comparing them with two traditional machine learning methods on the I-DAIC dataset. To overcome the difficulty of handling lengthy texts, we explore several customized strategies in combination with the advanced language models. Moreover, we conducted the quantitative analysis of key representative keywords using topic modeling for both non-depression and depression instances. The comprehensive experimental results demonstrated the effectiveness, advantages, and significant potential of pre-trained language models for depression detection with narrative interviews.
AB - In recent years, the prevalence of mental health issues among young people has significantly increased, especially due to the consequences of COVID-19 pandemic and the widespread adoption of remote work arrangements. Depression has emerged as a major global mental health concern due to its potentially devastating consequences. However, existing methods for depression detection still face several challenges, such as limited data availability, imbalanced labels, and inadequate consideration of contextual information. To tackle these challenges, in this paper, we first create a larger dataset, namely I-DAIC, for depression detection by integrating three existing datasets in the literature. We further fine-tune and examine two pre-trained transformer-based language models by comparing them with two traditional machine learning methods on the I-DAIC dataset. To overcome the difficulty of handling lengthy texts, we explore several customized strategies in combination with the advanced language models. Moreover, we conducted the quantitative analysis of key representative keywords using topic modeling for both non-depression and depression instances. The comprehensive experimental results demonstrated the effectiveness, advantages, and significant potential of pre-trained language models for depression detection with narrative interviews.
KW - Depression detection
KW - fine-tuned language models
KW - long text
KW - narrative interviews
UR - http://www.scopus.com/inward/record.url?scp=85184866665&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85184866665&partnerID=8YFLogxK
U2 - 10.1109/BIBM58861.2023.10385480
DO - 10.1109/BIBM58861.2023.10385480
M3 - Conference contribution
AN - SCOPUS:85184866665
T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
SP - 3173
EP - 3180
BT - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
A2 - Jiang, Xingpeng
A2 - Wang, Haiying
A2 - Alhajj, Reda
A2 - Hu, Xiaohua
A2 - Engel, Felix
A2 - Mahmud, Mufti
A2 - Pisanti, Nadia
A2 - Cui, Xuefeng
A2 - Song, Hong
T2 - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Y2 - 5 December 2023 through 8 December 2023
ER -