机器学习与深度学习在心理健康障碍早期检测与管理的进展 Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder ——《情感障碍报告杂志》第25卷，2026年7月—— <Journal of Affective Disorders Reports>, Vol. 25, July 2026 【摘要】深度学习（DL）与机器学习（ML）的融合在心理健康疾患的早期识别、诊断及治疗方面正发挥着重要作用。通过分析来自影像学、遗传学及行为评估的复杂数据，这些技术有望显著改善临床疗效。然而，它们在数据整合与伦理问题方面也带来了独特的挑战。本文综述了用于心理健康问题早期诊断与治疗的ML和DL方法的发展现状。文章探讨了多种应用场景，重点关注行为评估、遗传与生物标志物分析，以及针对抑郁症、双相情感障碍和精神分裂症等疾病诊断的医学影像分析。此外，本文还讨论了疾病发展预测建模的相关内容，重点分析了风险预测模型与纵向研究的作用。重要研究发现表明，ML和DL不仅能提高治疗效果和诊断准确性，还能应对方法学不一致、数据整合及伦理考量等方面的挑战。本研究强调了构建用于个性化治疗的实时监测系统、改进数据融合技术以及加强跨学科合作的重要性。未来的研究应致力于克服上述障碍，以实现ML和DL在心理健康服务中既有价值又符合伦理规范的应用。 【关键词】人工智能（AI）；心理健康诊断；预测建模；行为评估；数据整合 [Abstract] For the early identification, diagnosis, and treatment of mental health illnesses, the integration of deep learning (DL) and machine learning (ML) have started playing a significant role. By evaluating complex data from imaging, genetics, and behavioral assessments, these technologies have the potential to improve clinical results significantly. However, they also present unique challenges relating to data integration and ethical issues. The development of ML and DL methods for the early diagnosis and treatment of mental health issues is reviewed in this survey. It examines a range of applications, with a particular emphasis on behavioral assessments, genetic and biomarker analysis, and medical imaging for the diagnosis of diseases like depression, bipolar disorder, and schizophrenia. Predictive modeling for illness development is further discussed in the review, focusing on the function of risk prediction models and longitudinal investigations. Important discoveries show how ML and DL might improve treatment outcomes and diagnostic accuracy while tackling methodological inconsistency, data integration, and ethical concerns. The study emphasizes the significance of building real-time monitoring systems for individualized treatment, improving data fusion techniques, and interdisciplinary collaboration. Upcoming studies should concentrate on surmounting these obstacles to maximize ML and DL’s valuable and moral implementation in mental health services. [Key words] AI, Mental health diagnosis, Predictive modeling, Behavioral assessments, Data integration 论文原文：Kamala Devi Kannan, Senthil Kumar Jagatheesaperumal, Rajesh N.V.P.S. Kandala, et al. (2026). Advancements in machine learning and deep learning for early detection and management of mental health disorder. Journal of Affective Disorders Reports, Volume 25, July 2026. https://doi.org/10.1016/j.jadr.2026.101100 （翻译兼责任编辑：MARY） （需要英文原文的朋友，请联系微信：millerdeng95或iacmsp）
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