AI-based Intelligent Diagnosis System for Adolescent Mental Health Based on Multi-task Deep Learning

WENYUE LIU^1,2Zhihao Zhang²LINKANG DU³JIANGUO QIU^4*

• ¹Department of Physical Education, Yantai Institute of Science and Technology, Yantai, China
• ²Faculty of Education, Universiti Kebangsaan Malaysia, Bangi, Malaysia
• ³School of Cyber Science and Engineering, Xi'an Jiaotong University, Xi'an, China
• ⁴Faculty of Physical Education, Ludong University, Yantai, China

Background and Objectives: Adolescent depression and anxiety are becoming increasingly prevalent in China, with rates reaching 20–30\%, driven largely by intense academic pressure and the cultural tendency toward somatization. Traditional screening tools, such as the PHQ-9 and GAD-7, often suffer from subjective bias, recall errors, and underreporting due to social stigma. This study developed an AI-based Intelligent Diagnosis System (IDS) using multi-task deep learning to non-intrusively predict comorbid depression and anxiety severity based on the spontaneous textual expressions of Chinese adolescents.\\ Methods: Textual responses from approximately 1,275 adolescents were collected and labeled with clinician-assessed PHQ-9 and GAD-7 scores. Preprocessing involved jieba segmentation and VAE-based (Variational Autoencoder) data augmentation to address class imbalance, resulting in an expanded test set of 308 samples. The IDS architecture utilizes a Chinese-optimized BERT encoder with self-attention and dual-feature fusion (combining pooled [CLS] tokens and global pooling) to extract shared representations. These are processed through multi-task heads for regression (MSE loss) and classification (weighted cross-entropy). The model was trained using an 8:1:1 split with AdamW optimization, cosine annealing, and regularization, supported by ablation studies to validate individual components.\\ Results: On the test set, the IDS achieved Pearson correlation coefficients of 0.706 for PHQ-9 and 0.693 for GAD-7, with AUC values of 0.877 and 0.902, respectively. Binary classification yielded F1-scores of 0.762 (PHQ-9) and 0.863 (GAD-7). Ablation analysis confirmed that the multi-task learning framework improved F1-scores by 6.2–7.8\% and reduced MSE by 14.2–18.4\%. Furthermore, adaptations for somatization and data augmentation for severe cases significantly enhanced the system's sensitivity.\\ Conclusion: The IDS offers a robust, culturally sensitive, and scalable tool for adolescent mental health screening. By outperforming single-task baselines, it provides a proactive, privacy-preserving alternative to traditional self-reports. Future research will focus on longitudinal validation, multimodal integration, and ethical deployment strategies to maximize the system's utility in educational and clinical settings.