WATCH-SS:开发一个可靠和可解释的模块化框架来检测自发言语的认知障碍
Sydney Pugh1, Matthew Hill2, Sy Hwang2
1Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA, Sydney.Pugh@PennMedicine.upenn.edu.
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 27, 2026
概括
早期发现认知障碍 (CI) 对阿尔茨海默病的干预至关重要. WATCH-SS工具分析CI指标的语音,在早期查的关键数据集上达到80%的准确性.
科学领域:
- 神经学 神经学
- 计算语言学 计算语言学
- 人工智能在医学中的应用
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采用了三阶段的框架来分析语言和声学语音特征.
- 它结合了多种探测器方法,从实时高效到高精度离线方法.
- 一个预测模型基于聚合的,临床上可解释的总结特征来对CI进行分类.
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:
- WATCH-SS显示出作为初级保健机构可靠和临床可用的查工具的潜力.
- 这种基于语音的分析代表了早期CI检测的重大进步.
- 该工具的可解释性支持临床采用和患者信任.
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