初级保健患者与提供者对话的声学分析,以选认知障碍
Joseph T Colonel1, Jacqueline Becker2, Lili Chan2
1Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
medRxiv : the preprint server for health sciences
|January 8, 2026
概括
使用机器学习 (ML) 对患者和医生对话的被动分析可以在初级保健中识别认知障碍 (CI). 声学语音特征准确检测CI,支持常规查的可行性.
科学领域:
- 老年学是指老年学的学科.
- 计算语言学 计算语言学
- 主要护理医学 医疗保健
背景情况:
- 由于时间和资源的限制,认知障碍 (CI) 在初级保健中经常被诊断不足.
- 临床对话的被动分析提供了一个潜在的查解决方案.
研究的目的:
- 评估使用患者与医生对话的音频录制来识别CI的有效性.
- 评估声学语音特征和机器学习 (ML) 对CI检测的有用性.
主要方法:
- 一项观察性研究记录了55岁以上患者的常规初级保健访问.
- 从语音段中提取声学特征,使用基础模型 (例如,Whisper) 和专家定义的方法.
- 机器学习分类器被训练来预测CI状态,通过AUC-ROC和Fmax测量性能.
主要成果:
- 使用来自Whisper的声学特征的ML模型在识别CI方面取得了很高的准确性 (AUC-ROC=0.733).
- 对外部验证队列进行概括的性能 (AUC-ROC=0.727).
- 关键预测因素包括音调,时机和语音的变化.
结论:
- 从临床对话中分析声学特征的机器学习模型可以准确地识别认知障碍.
- 这种方法证明了在初级保健机构中被动的,基于语音的CI查的可行性.
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