機器學習與深度學習在心理健康障礙早期檢測與管理的進展 Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder ——《情感障礙報告雜誌》第25卷，2026年7月—— <Journal of Affective Disorders Reports>, Vol. 25, July 2026 【摘要】深度學習（DL）與機器學習（ML）的融合在心理健康疾患的早期識別、診斷及治療方面正發揮着重要作用。通過分析來自影像學、遺傳學及行為評估的複雜數據，這些技術有望顯著改善臨床療效。然而，它們在數據整合與倫理問題方面也帶來了獨特的挑戰。本文綜述了用於心理健康問題早期診斷與治療的ML和DL方法的發展現狀。文章探討了多種應用場景，重點關注行為評估、遺傳與生物標誌物分析，以及針對抑鬱症、雙相情感障礙和精神分裂症等疾病診斷的醫學影像分析。此外，本文還討論了疾病發展預測建模的相關內容，重點分析了風險預測模型與縱向研究的作用。重要研究發現表明，ML和DL不僅能提高治療效果和診斷準確性，還能應對方法學不一致、數據整合及倫理考量等方面的挑戰。本研究強調了構建用於個性化治療的實時監測系統、改進數據融合技術以及加強跨學科合作的重要性。未來的研究應致力於克服上述障礙，以實現ML和DL在心理健康服務中既有價值又符合倫理規範的應用。 【關鍵詞】人工智能（AI）；心理健康診斷；預測建模；行為評估；數據整合 [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 論文原文：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 （翻譯兼責任編輯：MARY） （需要英文原文的朋友，請聯繫微信：millerdeng95或iacmsp）
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