在大型付款者-提供者系统中检测抑郁症严重性的数字表型:对言语和语言模型表现的回顾性研究
Bradley Karlin1,2, Doug Henry1, Ryan Anderson1
1Highmark Health, 120 Fifth Avenue, Fifth Avenue Place, Pittsburgh, PA, 15222-3099, United States, 1 (412) 544-7000.
JMIR AI
|July 3, 2025
概括
语音分析准确地检测了2000多个现实世界的互动中的抑郁症严重程度. 这种机器学习模型显示了改善抑郁症识别和个性化治疗的潜力.
科学领域:
- 心理健康中的数字生物标志物.
- 机器学习在医疗保健中的应用.
- 用于心理评估的语音分析.
背景情况:
- 精确的抑郁症检测和测量是至关重要的.
- 语音分析为抑郁症提供了一个有前途的数字生物标志物.
- 之前的研究仅限于小规模的受控研究.
研究的目的:
- 评估用于抑郁症预测的机器学习 (ML) 模型.
- 在现实世界的互动中分析语义和声学语音特性.
- 评估不同的人口结构和社会经济因素的模型性能.
主要方法:
- 分析了2086个案件管理电话记录.
- 在语音数据上使用了ML模型,不包括患者健康问卷-9 (PHQ-9) 调查部分.
- 使用开发 (Dev) 和盲人组与患者健康问卷-8 (PHQ-8) 分数验证的模型性能.
主要成果:
- 实现了强大的模型性能,一致性相关系数 (ρc) 为0.57 (Dev集) 和0.54 (盲集).
- 在各种年龄组,性别和社会脆弱性指数类别 (ρc=0.44-0.61) 中表现一致.
- 接收器运行特征曲线下面的面积值在0.79到0.83之间,对于不同的抑郁症严重程度值.
结论:
- 语音分析对抑郁症的检测和严重程度的测量具有重大潜力.
- 这些发现支持在不同人群中使用语音生物标志物.
- 这项技术可以增强治疗,临床决策和个性化护理.
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