机器学习助力精神疾病早期诊断，提升治疗精准度。
Advancements in Machine Learning and Deep Learning for Early Detection and Management of Mental Health Disorder
- 融合行为、基因与影像数据，构建多模态预测模型。
- 提升抑郁症、双相障碍等疾病的诊断准确率与风险预测能力。
- 适合临床研究者与精神健康科技开发者参考。
机器学习（ML）与深度学习（DL）在精神疾病早期识别、诊断和治疗中发挥重要作用。通过分析来自影像、基因及行为评估的复杂数据，这些技术有望显著改善临床效果。本文综述了相关进展，重点涵盖行为评估、基因与生物标志物分析、医学影像在抑郁症、双相情感障碍和精神分裂症诊断中的应用。还讨论了疾病发展预测的建模方法，强调风险预测模型与纵向研究的作用。研究表明，ML与DL可提升治疗效果与诊断准确性，同时需应对方法不一致、数据整合与伦理挑战。研究呼吁建立实时个体化监测系统，改进数据融合技术，加强跨学科合作。未来研究应聚焦克服这些障碍，推动机器学习与深度学习在精神健康服务中的有效且合乎伦理的应用。
原文摘要 · Abstract (English)
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.
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