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PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children

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Pubblicato in:arXiv.org (Dec 19, 2024), p. n/a
Autore principale: Zhang, Yiqun
Altri autori: Yang, Xiaocui, Li, Xiaobai, Yu, Siyuan, Luan, Yi, Shi, Feng, Wang, Daling, Zhang, Yifei
Pubblicazione:
Cornell University Library, arXiv.org
Soggetti:
Feature extraction
Mental health
Drawings
Multiagent systems
Professionals
Large language models
Medical screening
Medical personnel
Children
Accesso online:Citation/Abstract
Full text outside of ProQuest
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022 |a 2331-8422 
035 |a 3147565128 
045 0 |b d20241219 
100 1 |a Zhang, Yiqun 
245 1 |a PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children 
260 |b Cornell University Library, arXiv.org  |c Dec 19, 2024 
513 |a Working Paper 
520 3 |a Left-behind children (LBCs), numbering over 66 million in China, face severe mental health challenges due to parental migration for work. Early screening and identification of at-risk LBCs is crucial, yet challenging due to the severe shortage of mental health professionals, especially in rural areas. While the House-Tree-Person (HTP) test shows higher child participation rates, its requirement for expert interpretation limits its application in resource-scarce regions. To address this challenge, we propose PsyDraw, a multi-agent system based on Multimodal Large Language Models that assists mental health professionals in analyzing HTP drawings. The system employs specialized agents for feature extraction and psychological interpretation, operating in two stages: comprehensive feature analysis and professional report generation. Evaluation of HTP drawings from 290 primary school students reveals that 71.03% of the analyzes achieved High Consistency with professional evaluations, 26.21% Moderate Consistency and only 2.41% Low Consistency. The system identified 31.03% of cases requiring professional attention, demonstrating its effectiveness as a preliminary screening tool. Currently deployed in pilot schools, \method shows promise in supporting mental health professionals, particularly in resource-limited areas, while maintaining high professional standards in psychological assessment. 
653 |a Feature extraction 
653 |a Mental health 
653 |a Drawings 
653 |a Multiagent systems 
653 |a Professionals 
653 |a Large language models 
653 |a Medical screening 
653 |a Medical personnel 
653 |a Children 
700 1 |a Yang, Xiaocui 
700 1 |a Li, Xiaobai 
700 1 |a Yu, Siyuan 
700 1 |a Luan, Yi 
700 1 |a Shi, Feng 
700 1 |a Wang, Daling 
700 1 |a Zhang, Yifei 
773 0 |t arXiv.org  |g (Dec 19, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3147565128/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.14769