  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.
