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.

设计了一款安卓应用程序，结合小游戏和聊天记录的机器人，对 3,783 名中学生进行了综合式多模态筛查。通过引入新的注意机制，开发了名为 GAME 的模型，能够以高准确度（73.34%-92.77%）和 F1 值（71.32%-91.06%）评估青少年的精神状态。研究发现每种模态对于精神疾病筛查和共病症的贡献不同，表明可解释模型具有潜在的可行性。该研究提供了一套能够获取多模态信息并构建综合式多模态集成算法的系统，用于对青少年精神疾病进行早期筛查。