Classifying Anxiety and Depression through LLMs Virtual Interactions: A Case Study with ChatGPT

Yongfeng Tao; Minqiang Yang; Hao Shen; Zhichao Yang; Ziru Weng; Bin Hu

doi:10.1109/BIBM58861.2023.10385305

Classifying Anxiety and Depression through LLMs Virtual Interactions: A Case Study with ChatGPT

Yongfeng Tao, Minqiang Yang^*, Hao Shen, Zhichao Yang, Ziru Weng, Bin Hu^*

^*Corresponding author for this work

Lanzhou University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

Mental health has long been studied, and many AI-based approaches have been proposed for diagnosis and adjunctive therapy. The emergence of Pre-trained Large Language Models (LLMs) has had a profound impact on various fields, but the potential of using ChatGPT for cognitive behavior therapy is largely unexplored. Therefore, there is an urgent need to build and design a virtual interactive framework for assisted diagnosis/treatment. In this paper, we present a virtual interaction framework based on LLMs that allows participants to engage in a dialogue with a virtual character, analyse mental health issues through augmented LLMs, and make suggestions during the dialogue to alleviate the psychological problems they are currently facing. Based on this framework, we develop a use case for the application of ChatGPT in the field of emotional disorders. Specifically, we use data from question-and-answer dialogues in real-life scenarios to populate the current exploration of ChatGPT's potential for depression and anxiety detection. The case study shows the great potential of ChatGPT in the analysis of depression and anxiety tests. The feasibility of a virtual interaction framework based on LLMs has been preliminarily demonstrated.

Original language	English
Title of host publication	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Editors	Xingpeng Jiang, Haiying Wang, Reda Alhajj, Xiaohua Hu, Felix Engel, Mufti Mahmud, Nadia Pisanti, Xuefeng Cui, Hong Song
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2259-2264
Number of pages	6
ISBN (Electronic)	9798350337488
DOIs	https://doi.org/10.1109/BIBM58861.2023.10385305
Publication status	Published - 2023
Externally published	Yes
Event	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 - Istanbul, Turkey Duration: 5 Dec 2023 → 8 Dec 2023

Publication series

Name	Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

Conference

Conference	2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023
Country/Territory	Turkey
City	Istanbul
Period	5/12/23 → 8/12/23

Keywords

affective disorders
anxiety detection
depression detection
virtual character

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/BIBM58861.2023.10385305

Cite this

Tao, Y., Yang, M., Shen, H., Yang, Z., Weng, Z., & Hu, B. (2023). Classifying Anxiety and Depression through LLMs Virtual Interactions: A Case Study with ChatGPT. In X. Jiang, H. Wang, R. Alhajj, X. Hu, F. Engel, M. Mahmud, N. Pisanti, X. Cui, & H. Song (Eds.), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 (pp. 2259-2264). (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM58861.2023.10385305

Tao, Yongfeng ; Yang, Minqiang ; Shen, Hao et al. / Classifying Anxiety and Depression through LLMs Virtual Interactions : A Case Study with ChatGPT. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. editor / Xingpeng Jiang ; Haiying Wang ; Reda Alhajj ; Xiaohua Hu ; Felix Engel ; Mufti Mahmud ; Nadia Pisanti ; Xuefeng Cui ; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 2259-2264 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

@inproceedings{143948d823dc4be79baf47e681298bc8,

title = "Classifying Anxiety and Depression through LLMs Virtual Interactions: A Case Study with ChatGPT",

abstract = "Mental health has long been studied, and many AI-based approaches have been proposed for diagnosis and adjunctive therapy. The emergence of Pre-trained Large Language Models (LLMs) has had a profound impact on various fields, but the potential of using ChatGPT for cognitive behavior therapy is largely unexplored. Therefore, there is an urgent need to build and design a virtual interactive framework for assisted diagnosis/treatment. In this paper, we present a virtual interaction framework based on LLMs that allows participants to engage in a dialogue with a virtual character, analyse mental health issues through augmented LLMs, and make suggestions during the dialogue to alleviate the psychological problems they are currently facing. Based on this framework, we develop a use case for the application of ChatGPT in the field of emotional disorders. Specifically, we use data from question-and-answer dialogues in real-life scenarios to populate the current exploration of ChatGPT's potential for depression and anxiety detection. The case study shows the great potential of ChatGPT in the analysis of depression and anxiety tests. The feasibility of a virtual interaction framework based on LLMs has been preliminarily demonstrated.",

keywords = "affective disorders, anxiety detection, depression detection, virtual character",

author = "Yongfeng Tao and Minqiang Yang and Hao Shen and Zhichao Yang and Ziru Weng and Bin Hu",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023 ; Conference date: 05-12-2023 Through 08-12-2023",

year = "2023",

doi = "10.1109/BIBM58861.2023.10385305",

language = "English",

series = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "2259--2264",

editor = "Xingpeng Jiang and Haiying Wang and Reda Alhajj and Xiaohua Hu and Felix Engel and Mufti Mahmud and Nadia Pisanti and Xuefeng Cui and Hong Song",

booktitle = "Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023",

address = "United States",

}

Tao, Y, Yang, M, Shen, H, Yang, Z, Weng, Z & Hu, B 2023, Classifying Anxiety and Depression through LLMs Virtual Interactions: A Case Study with ChatGPT. in X Jiang, H Wang, R Alhajj, X Hu, F Engel, M Mahmud, N Pisanti, X Cui & H Song (eds), Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Institute of Electrical and Electronics Engineers Inc., pp. 2259-2264, 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023, Istanbul, Turkey, 5/12/23. https://doi.org/10.1109/BIBM58861.2023.10385305

Classifying Anxiety and Depression through LLMs Virtual Interactions: A Case Study with ChatGPT. / Tao, Yongfeng; Yang, Minqiang; Shen, Hao et al.
Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. ed. / Xingpeng Jiang; Haiying Wang; Reda Alhajj; Xiaohua Hu; Felix Engel; Mufti Mahmud; Nadia Pisanti; Xuefeng Cui; Hong Song. Institute of Electrical and Electronics Engineers Inc., 2023. p. 2259-2264 (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Classifying Anxiety and Depression through LLMs Virtual Interactions

T2 - 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

AU - Tao, Yongfeng

AU - Yang, Minqiang

AU - Shen, Hao

AU - Yang, Zhichao

AU - Weng, Ziru

AU - Hu, Bin

PY - 2023

Y1 - 2023

N2 - Mental health has long been studied, and many AI-based approaches have been proposed for diagnosis and adjunctive therapy. The emergence of Pre-trained Large Language Models (LLMs) has had a profound impact on various fields, but the potential of using ChatGPT for cognitive behavior therapy is largely unexplored. Therefore, there is an urgent need to build and design a virtual interactive framework for assisted diagnosis/treatment. In this paper, we present a virtual interaction framework based on LLMs that allows participants to engage in a dialogue with a virtual character, analyse mental health issues through augmented LLMs, and make suggestions during the dialogue to alleviate the psychological problems they are currently facing. Based on this framework, we develop a use case for the application of ChatGPT in the field of emotional disorders. Specifically, we use data from question-and-answer dialogues in real-life scenarios to populate the current exploration of ChatGPT's potential for depression and anxiety detection. The case study shows the great potential of ChatGPT in the analysis of depression and anxiety tests. The feasibility of a virtual interaction framework based on LLMs has been preliminarily demonstrated.

AB - Mental health has long been studied, and many AI-based approaches have been proposed for diagnosis and adjunctive therapy. The emergence of Pre-trained Large Language Models (LLMs) has had a profound impact on various fields, but the potential of using ChatGPT for cognitive behavior therapy is largely unexplored. Therefore, there is an urgent need to build and design a virtual interactive framework for assisted diagnosis/treatment. In this paper, we present a virtual interaction framework based on LLMs that allows participants to engage in a dialogue with a virtual character, analyse mental health issues through augmented LLMs, and make suggestions during the dialogue to alleviate the psychological problems they are currently facing. Based on this framework, we develop a use case for the application of ChatGPT in the field of emotional disorders. Specifically, we use data from question-and-answer dialogues in real-life scenarios to populate the current exploration of ChatGPT's potential for depression and anxiety detection. The case study shows the great potential of ChatGPT in the analysis of depression and anxiety tests. The feasibility of a virtual interaction framework based on LLMs has been preliminarily demonstrated.

KW - affective disorders

KW - anxiety detection

KW - depression detection

KW - virtual character

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U2 - 10.1109/BIBM58861.2023.10385305

DO - 10.1109/BIBM58861.2023.10385305

M3 - Conference contribution

AN - SCOPUS:85184866409

T3 - Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

SP - 2259

EP - 2264

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

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 5 December 2023 through 8 December 2023

ER -

Tao Y, Yang M, Shen H, Yang Z, Weng Z, Hu B. Classifying Anxiety and Depression through LLMs Virtual Interactions: A Case Study with ChatGPT. In Jiang X, Wang H, Alhajj R, Hu X, Engel F, Mahmud M, Pisanti N, Cui X, Song H, editors, Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 2259-2264. (Proceedings - 2023 2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023). doi: 10.1109/BIBM58861.2023.10385305

Classifying Anxiety and Depression through LLMs Virtual Interactions: A Case Study with ChatGPT

Abstract

Publication series

Conference

Keywords

UN SDGs

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