从文本和音频中自动检测抑郁症:系统性审查
IEEE journal of biomedical and health informatics
|May 16, 2025
概括
自动抑郁检测 (ADD) 系统分析文本和语音,以进行可扩展的心理健康评估. 本次审查强调了对文化敏感,可解释的AI模型的需求,以改善临床应用.
科学领域:
- 人工智能在心理健康中的作用
- 计算语言学 计算语言学
- 机器学习用于医疗保健
背景情况:
- 抑郁症是一种广泛的心理健康问题,需要有效的诊断和治疗.
- 自动抑郁检测 (ADD) 系统使用文本和音频数据提供可扩展的解决方案.
- 当前的挑战包括对抑郁症的及时诊断和干预.
研究的目的:
- 使用多式联网数据系统地审查基于机器学习的ADD系统.
- 分析数据增强,多式联接和特征提取等方法.
- 为了确定当前的趋势和未来的研究方向在自动抑郁症检测.
主要方法:
- 对65项研究 (2018-2024) 的系统文献综述.
- 专注于使用多式联络 (文本和音频) 数据的机器学习模型.
- 检查数据增强,融合技术和特征提取策略.
主要成果:
- 确定了关键的方法和最先进的ADD系统.
- 强调文化适应性,高质量的数据集的重要性.
- 在纵向数据和现实世界临床整合方面注意到局限性.
结论:
- 未来的ADD系统需要用于临床使用的可解释性,可扩展性和稳定性.
- 重点是开发跨文化和临床综合的抑郁症检测工具.
- 该审查提供了全面的概述,并确定了促进ADD的研究差距.
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