一个可解释的机器学习模型的认知衰退来源于语音演讲.
Chelsea Chandler1, Catherine Diaz-Asper2, Raymond S Turner3
1Institute of Cognitive Science University of Colorado Boulder Colorado USA.
Alzheimer's & dementia (Amsterdam, Netherlands)
|December 29, 2023
概括
分析语音数据的人工智能 (AI) 模型可以检测认知衰退,包括阿尔茨海默病 (AD) 和轻度认知障碍 (MCI),准确率为75%. 这种多式联络方式提供了一种比传统工具更灵敏,更及时的选方法.
科学领域:
- 神经科学是一个神经科学.
- 人工智能的人工智能
- 计算语言学 计算语言学
背景情况:
- 传统的阿尔茨海默病 (AD) 和轻度认知障碍 (MCI) 查往往缺乏早期发现认知衰退所需的敏感性和及时性.
- 多模式人工智能 (AI) 利用语音数据为增强神经退行性疾病检测提供了一个有希望的途径.
研究的目的:
- 开发和评估一个人工智能驱动的方法,以提高认知衰退的查,仅使用语音数据.
- 评估多式人工智能模型在区分认知健康个体,阿兹海默氏症患者和有amnestic MCI (aMCI) 患者之间的准确性.
主要方法:
- 通过电话从91名参与者收集了语音数据:29名认知健康,30名患有AD,32名患有MCI.
- 采用了多模式自然语言和语音处理方法.
- 开发了一种可解释的集合决策树分类器,用于多类预测认知衰退.
主要成果:
- 人工智能模型在分类认知状态方面实现了75%的整体准确性.
- 这种准确性比传统的基于语音的查和基于单模式语言的模型有所改善.
- 开发了一个仪表板,用于检查结果并以新的方式解释数据.
结论:
- 开发的AI方法论为临床实践中认知衰退查的重大进展提供了基础.
- 临床翻译,可扩展性和用户友好性的核心考虑因素被整合到研究的设计中.
- 通过人工智能模型进行远程评估可以增加对认知衰退查的可访问性,在诊断组中观察到语音模式的显著差异.
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