WATCH-SS:开发一种可靠和可解释的模块化框架,用于从自发言语中检测认知障碍
Sydney Pugh1, Matthew Hill1, Sy Hwang1
1Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA USA.
medRxiv : the preprint server for health sciences
|November 24, 2025
概括
早期发现认知障碍 (CI) 是至关重要的. 该工具分析语音CI指标,达到80%的准确性,为可靠的初级保健查铺平了道路.
科学领域:
- 神经学 神经学
- 计算语言学 计算语言学
- 人工智能的人工智能
背景情况:
- 早期发现认知障碍 (CI) 对管理阿尔茨海默病和相关痴呆症至关重要.
- 目前的查方法可能缺乏可访问性或效率,无法广泛使用初级保健.
研究的目的:
- 引入自发言语对认知健康的警告评估和警报工具 (WATCH-SS).
- 开发一个模块化,可解释的框架,用语音分析来检测CI.
主要方法:
- WATCH-SS框架采用了三个阶段的过程,用于五种语言和声学CI指标的探测器.
- 输出被汇总成临床可解释的特征,用于预测性CI分类模型.
- 探索了高效的实时和高精度离线分析方法.
主要成果:
- 在DementiaBank ADReSS数据集上,WATCH-SS框架表现出强大的预测性能.
- 在测试组中,在曲线下的面积 (AUC) 达到80%.
- 模块化,基于特征的方法提供了一个透明的诊断档案.
结论:
- 该WATCH-SS工具显示显著的潜力作为一个值得信赖的和临床可用的查工具,用于初级保健.
- 这种方法提供了一个透明的诊断形状,增强了临床信任.
- 进一步开发可能会促进对认知障碍的早期干预.
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