SBT-Net:用于抑郁症识别的三维引导的多式联络融合框架
Yujie Huo1, Weng Howe Chan2,3, Ahmad Najmi Bin Amerhaider Nuar1
1Faculty of Computing, Universiti Teknologi Malaysia, UTM Skudai, Johor Bahru, Johor, 81310, Malaysia.
BioData mining
|December 23, 2025
概括
这项研究介绍了SBT-Net,这是使用音频和文本检测抑郁症的新框架. 它通过整合语义指导,偏见意识融合和情绪趋势建模来实现高准确度,以进行强大的多式联络分析.
科学领域:
- 计算精神病学是一种计算精神病学.
- 医疗保健中的人工智能
- 多模式机器学习是多模式机器学习.
背景情况:
- 早期发现抑郁症对公共健康至关重要.
- 当前的多式联络方法面临的挑战包括不完整的数据,语义上的不一致性和波动的情绪状态.
- 强大的抑郁症检测需要先进的分析框架.
研究的目的:
- 提出SBT-Net,这是一个新的语义偏差趋势指导框架,用于使用音频和文本数据进行强大的抑郁症检测.
- 为了解决现有的多式联通低谷检测方法的局限性.
- 提高自动化抑郁症评估的准确性和可靠性.
主要方法:
- 开发了SBT-Net,结合了用于特征过的语义引导跨模态门 (SGCMG) 机制.
- 集成了一个偏差导向的张量产品注意力 (BG-TPA) 机制,用于增强模式间融合和对齐.
- 利用情绪趋势建模 (ETM) 模块捕捉抑郁状态的时间动态.
主要成果:
- 在基准数据集 (DAIC-WOZ,EATD-Corpus) 上,SBT-Net实现了93.0%的准确性,0.93的F1得分和0.92的回忆.
- 在多个评估指标上,性能超过了竞争基线模型.
- 废弃性研究证实了单个和组合模块的显著贡献.
结论:
- 拟议的SBT-Net框架在多式联络抑郁症检测方面表现出卓越的性能.
- 整合语义指导,偏见意识融合和情绪趋势建模可以提高稳定性.
- 这些发现表明,对于推进自动化心理健康监测解决方案的有希望的方向.
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