辐射:使用变压器从语音中可靠和可解释的抑郁症检测
Anup Kumar Gupta1, Ashutosh Dhamaniya1, Puneet Gupta1
1Department of Computer Science and Engineering, Indian Institute of Technology Indore, Indore, 452020, Madhya Pradesh, India.
Computers in biology and medicine
|November 3, 2024
概括
一种名为RADIANCE (可靠和可解释的抑郁症检测转换器) 的新方法使用语音可靠地检测抑郁症. 它提供了可解释的结果,并且在准确性方面优于现有的方法.
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
- 语音信号处理 语音信号处理
背景情况:
- 抑郁症是一种普遍存在的精神障碍,严重影响日常功能.
- 由于耻辱和有限的医疗保健机会,许多病例仍未被诊断出来.
- 现有的基于语音检测抑郁症的深度学习模型缺乏透明度.
研究的目的:
- 开发一种可靠和可解释的深度学习模型,用于使用语音自动检测抑郁症.
- 解决临床环境中黑子模型的局限性.
- 提高人工智能驱动的心理健康评估的准确性和可靠性.
主要方法:
- 介绍了RADIANCE (可靠和可解释的抑郁检测变换器),其中包括FilterBank视觉变换器 (FBViT) 用于可解释的症状识别.
- 实施一种新的损失函数来管理类不平衡和错误分类等级.
- 开发使用低级别描述符的可靠性预测器,以量化预测可靠性并增强多片段音频分析.
主要成果:
- 雷迪安斯实现了最先进的性能,精度为89.36% (DAIC-WOZ),80.36% (E-DAIC) 和94.44% (CMDC).
- 平均绝对误差 (MAE) 评分为3.27 (DAIC-WOZ) 和5.04 (E-DAIC) 证明了预测的准确性.
- 该模型提供可解释的抑郁症症状和可靠性得分,用于临床实用性.
结论:
- RADIANCE提供了一种透明而准确的方法,用于基于语音的抑郁症检测.
- 该方法有效地处理数据挑战,如类不平衡和错误分类.
- 这种可解释的人工智能模型具有增强心理健康诊断的巨大潜力.
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