PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children
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arXiv
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| Auteurs principaux: | , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866916532722860032 |
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| author | Zhang, Yiqun Yang, Xiaocui Li, Xiaobai Yu, Siyuan Luan, Yi Feng, Shi Wang, Daling Zhang, Yifei |
| author_facet | Zhang, Yiqun Yang, Xiaocui Li, Xiaobai Yu, Siyuan Luan, Yi Feng, Shi Wang, Daling Zhang, Yifei |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_14769 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children Zhang, Yiqun Yang, Xiaocui Li, Xiaobai Yu, Siyuan Luan, Yi Feng, Shi Wang, Daling Zhang, Yifei Computation and Language 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. |
| title | PsyDraw: A Multi-Agent Multimodal System for Mental Health Screening in Left-Behind Children |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2412.14769 |