In contemporary society, the escalating pressures of life and work have propelled psychological disorders to the forefront of modern health concerns, an issue that has been further accentuated by the COVID-19 pandemic. The prevalence of depression among adolescents is steadily increasing, and traditional diagnostic methods, which rely on scales or interviews, prove particularly inadequate for detecting depression in young people. Addressing these challenges, numerous AI-based methods for assisting in the diagnosis of mental health issues have emerged. However, most of these methods center around fundamental issues with scales or use multimodal approaches like facial expression recognition. Diagnosis of depression risk based on everyday habits and behaviors has been limited to small-scale qualitative studies. Our research leverages adolescent census data to predict depression risk, focusing on children's experiences with depression and their daily life situations. We introduced a method for managing severely imbalanced high-dimensional data and an adaptive predictive approach tailored to data structure characteristics. Furthermore, we proposed a cloud-based architecture for automatic online learning and data updates. This study utilized publicly available NSCH youth census data from 2020 to 2022, encompassing nearly 150,000 data entries. We conducted basic data analyses and predictive experiments, demonstrating significant performance improvements over standard machine learning and deep learning algorithms. This affirmed our data processing method's broad applicability in handling imbalanced medical data. Diverging from typical predictive method research, our study presents a comprehensive architectural solution, considering a wider array of user needs.

翻译：在当代社会，日益加剧的生活和工作压力已将心理障碍推至现代健康问题的前沿，这一问题因新冠疫情而进一步凸显。青少年抑郁症患病率持续上升，而依赖量表或面谈的传统诊断方法在检测青少年抑郁方面尤为不足。针对这些挑战，众多基于人工智能的心理健康辅助诊断方法应运而生。然而，这些方法大多围绕量表基础问题或采用面部表情识别等多模态途径。基于日常习惯和行为进行抑郁风险诊断仅局限于小规模定性研究。本研究利用青少年人口普查数据预测抑郁风险，重点关注儿童的抑郁经历及其日常生活状况。我们提出了一种针对严重不平衡高维数据的管理方法，以及一种适应数据结构特征的自适应预测方法。此外，我们提出了一种用于自动化在线学习和数据更新的云架构。本研究使用了2020年至2022年公开可用的NSCH青少年人口普查数据，涵盖近15万条数据记录。我们进行了基础数据分析和预测实验，结果表明我们的方法在性能上显著优于标准机器学习和深度学习算法。这证实了我们的数据处理方法在处理不平衡医学数据方面具有广泛适用性。与典型的预测方法研究不同，本研究提出了一种全面的架构方案，考虑了更广泛的用户需求。