通过自然语言处理对帕金森病的数字表型化
Simona Aresta1, Petronilla Battista2, Cinzia Palmirotta1
1Istituti Clinici Scientifici Maugeri IRCCS, Laboratory of Neuropsychology, Institute of Bari, Bari, Italy.
NPJ Parkinson's disease
|June 23, 2025
概括
这项研究表明,人工智能可以使用语音模式检测帕金森病 (PD) 和认知障碍. 这些数字语言标记器为早期诊断和理解疾病亚型提供了潜力.
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:
- 帕金森氏病 (PD) 涉及前胸腺退化,导致语言缺陷.
- 当前的诊断方法往往忽略了诸如轻度认知障碍 (MCI) 这样的认知现象.
- 自然语言处理 (NLP) 为PD中的数字表型提供了一种新的方法.
研究的目的:
- 验证一种人工智能驱动的方法来识别PD中的数字语言标记.
- 要区分与MCI (PD-MCI) 和没有MCI (PD-nMCI) 的PD与健康对照 (HCs) 相比.
- 探索语言特征在早期PD诊断和表型化方面的潜力.
主要方法:
- 对PD患者和HC患者的连接语音分析.
- 使用CLAN软件提取语言特征.
- 使用支持矢量机器 (SVM) 和递归特征消除 (RFE) 进行分类.
主要成果:
- 对于PD与HC的歧视,AI实现了77%的AUC.
- 小组分析显示AUC较高:85% (PD-nMCI与HC相比),83% (PD-MCI与HC相比) 和75% (PD-nMCI与PD-MCI相比).
- 显著的语言特征包括追溯,动作动词,发音错误和无动词发音比率.
结论:
- 基于人工智能的语音分析提供了PD的有效数字标记.
- 这种方法对早期诊断和PD的表型有希望,包括认知亚型.
- 需要对更大样本进行进一步的研究,以确认可概括性.
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