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arXiv · 2309.10077

GAME: Generalized deep learning model towards multimodal data integration for early screening of adolescent mental disorders

Zhicheng Du·Chenyao Jiang·Xi Yuan·Shiyao Zhai·Zhengyang Lei·Shuyue Ma·Yang Liu·Qihui Ye·Chufan Xiao·Qiming Huang·Ming Xu·Dongmei Yu·Peiwu Qin
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Abstract

The timely identification of mental disorders in adolescents is a global public health challenge.Single factor is difficult to detect the abnormality due to its complex and subtle nature. Additionally, the generalized multimodal Computer-Aided Screening (CAS) systems with interactive robots for adolescent mental disorders are not available. Here, we design an android application with mini-games and chat recording deployed in a portable robot to screen 3,783 middle school students and construct the multimodal screening dataset, including facial images, physiological signs, voice recordings, and textual transcripts.We develop a model called GAME (Generalized Model with Attention and Multimodal EmbraceNet) with novel attention mechanism that integrates cross-modal features into the model. GAME evaluates adolescent mental conditions with high accuracy (73.34%-92.77%) and F1-Score (71.32%-91.06%).We find each modality contributes dynamically to the mental disorders screening and comorbidities among various mental disorders, indicating the feasibility of explainable model. This study provides a system capable of acquiring multimodal information and constructs a generalized multimodal integration algorithm with novel attention mechanisms for the early screening of adolescent mental disorders.

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Zhicheng Du, Chenyao Jiang, Xi Yuan, Shiyao Zhai, Zhengyang Lei, Shuyue Ma, Yang Liu, Qihui Ye, Chufan Xiao, Qiming Huang, Ming Xu, Dongmei Yu, Peiwu Qin. 2023-09-18. GAME: Generalized deep learning model towards multimodal data integration for early screening of adolescent mental disorders. https://arxiv.org/abs/2309.10077

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