DPD (抑郁检测) 网:用于多模式抑郁检测的深度神经网络.
Manlu He1, Erwin M Bakker1, Michael S Lew1
1Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Niels Bohrweg 1, 2333CA Leiden, Netherlands.
Health information science and systems
|November 15, 2024
概括
开发了深度学习模型,用于使用多式联络数据自动检测抑郁症,从而提高了临床和社交媒体环境中的诊断准确性. 这些模型在基准数据集上显示出卓越的性能,有助于更强大的心理健康评估.
科学领域:
- 人工智能的人工智能
- 计算精神病学是一种计算精神病学.
- 机器学习用于医疗保健
背景情况:
- 抑郁症的诊断是复杂和主观的,通常依赖于医生采访.
- 由于单个数据集测试,现有的自动抑郁检测模型缺乏稳定性.
研究的目的:
- 提出深度学习模型,使用多式联网数据自动检测抑郁症.
- 为了提高抑郁症检测系统的稳定性和可扩展性.
- 为了协助临床诊断抑郁症.
主要方法:
- 开发了DePressionDetect Net (DPD Net),这是一个由图形神经网络增强的变压器模型,集成文本,音频和视觉功能.
- 拟议的DepressionDetect-with-EEG Net (DPD-E Net) 将电脑学 (EEG) 信号和语音数据纳入其中.
- 在临床和社交媒体环境中评估了四个基准数据集中的模型.
主要成果:
- 在三个数据集 (E-DAIC,Twitter,MODMA) 上,DPD Net和DPD-E Net的性能优于最先进的模型.
- 在D-vlog数据集上取得了竞争性表现.
- 废弃性研究证实了拟议的模块和多式联运集成的有效性.
结论:
- 拟议的深度学习模型为自动抑郁症检测提供了更强大,更可扩展的方法.
- 结合不同的数据模式,显著提高了抑郁症检测准确度.
- 这些模型可以作为有价值的工具来帮助诊断抑郁症.
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