对自然语言处理技术进行系统审查,以早期检测认知障碍
Ravi Shankar1, Anjali Bundele1, Amartya Mukhopadhyay2
1Medical Affairs-Research Innovation and Enterprise, Alexandra Hospital, National University Health System, Singapore.
Mayo Clinic proceedings. Digital health
|June 26, 2025
概括
自然语言处理 (NLP) 显示了使用语音分析检测早期认知衰退的前景. 结合语言和声学NLP方法提供了最高的准确性,但需要更多的研究来临床使用.
科学领域:
- 计算语言学计算语言学
- 医疗信息学医学信息学
- 神经科学是一个神经科学.
背景情况:
- 认知衰退是一个重大的公共卫生挑战.
- 早期检测对于及时干预和管理至关重要.
- 语音和语言分析提供了认知状态的非侵入性生物标志物.
研究的目的:
- 系统地评估自然语言处理 (NLP) 技术用于早期认知衰退的检测.
- 评估NLP在认知障碍的言语和语言分析中的有效性和方法方法.
主要方法:
- 对8个数据库进行了全面的系统审查.
- 包括使用NLP分析语音/语言数据以发现认知障碍的研究.
- 诊断准确度指标被提取并分析.
主要成果:
- 分析了51项涉及17340名参与者的研究.
- 综合语言声学NLP方法产生了最高的诊断准确度 (87%,AUC 0.89).
- 词汇多样性,语法复杂性和语义连贯性是关键预测因素.
结论:
- 在跨语言的认知障碍检测方面,NLP技术显示出有希望的准确性.
- 结合的语言声学NLP方法是最有效的.
- 需要进一步的大规模,标准化的研究来确定临床效用.
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