The application of machine learning (ML) in detecting, diagnosing, and treating mental health disorders is garnering increasing attention. Traditionally, research has focused on single modalities, such as text from clinical notes, audio from speech samples, or video of interaction patterns. Recently, multimodal ML, which combines information from multiple modalities, has demonstrated significant promise in offering novel insights into human behavior patterns and recognizing mental health symptoms and risk factors. Despite its potential, multimodal ML in mental health remains an emerging field, facing several complex challenges before practical applications can be effectively developed. This survey provides a comprehensive overview of the data availability and current state-of-the-art multimodal ML applications for mental health. It discusses key challenges that must be addressed to advance the field. The insights from this survey aim to deepen the understanding of the potential and limitations of multimodal ML in mental health, guiding future research and development in this evolving domain.

翻译：机器学习（ML）在心理健康障碍的检测、诊断与治疗中的应用正受到日益广泛的关注。传统研究通常聚焦于单一模态，例如临床记录中的文本、语音样本的音频或互动模式的视频。近年来，多模态机器学习通过整合来自多种模态的信息，在揭示人类行为模式、识别心理健康症状及风险因素方面展现出巨大潜力。尽管前景广阔，心理健康领域的多模态机器学习仍处于新兴阶段，在实现有效实际应用前尚面临诸多复杂挑战。本综述全面概述了心理健康领域的数据可用性及当前最先进的多模态机器学习应用，并探讨了推动该领域发展必须解决的关键挑战。本文旨在深化对多模态机器学习在心理健康领域潜力与局限性的理解，为这一不断演进领域的未来研究与发展提供指引。