人工智能从连接的语音中分类初级渐进性失言症
Neguine Rezaii1, Daisy Hochberg1, Megan Quimby1
1Frontotemporal Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
Brain : a journal of neurology
|June 24, 2024
概括
人工智能和自然语言处理在患有初级渐进性失言症 (PPA) 的患者中确定了不同的语音模式. 这种方法准确地分类了PPA变体和健康对照,改善了诊断能力.
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:
- 初级渐进性失言症 (PPA) 是一种神经退行性痴呆症候群.
- 诊断传统上依赖于认知特征,关于PPA变体分类的争论仍在进行中.
- 确定PPA变体分类的独特语言特征仍然是一个挑战.
研究的目的:
- 应用人工智能 (AI) 和自然语言处理 (NLP) 进行PPA语音样本的无监督分类.
- 确定区分PPA变体的关键语言特征.
- 为PPA变种和健康对照开发一个准确的分类器.
主要方法:
- 使用大型语言模型从78名PPA患者的语音样本进行无监督分类.
- NLP分析以识别分离的语言特征.
- 监督分类,包括识别的语言特征,包括动词频率,以分类PPA变体和对照.
主要成果:
- 大型语言模型确定了三个不同的PPA集群,与临床诊断达成88.5%一致.
- 数据驱动的集群中的皮质缩模式与临床诊断标准保持一致.
- 一个使用17个语言特征的分类器,特别是高频和低频动词,在分类四个组 (3个PPA变体,1个控制) 中获得了97.9%的准确性.
结论:
- 人工智能和NLP可以有效地将PPA患者的言语分为临床相关的组.
- 特定的语言特征,特别是动词频率,是PPA变体分类的强有力的指标.
- 这些发现提高了诊断准确度,并加深了对神经退行症中语言处理的理解.
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