使用语义语言内容和变压器深度学习架构进行认知衰退评估
1Sri Sivasubramaniya Nadar College of Engineering, Tamil Nadu, India.
International journal of language & communication disorders
|November 16, 2023
概括
这项研究引入了一种新的深度学习方法,使用语音分析来检测痴呆症的早期迹象. 同位数相似度指标有效地识别了语音中的语义相关性,有助于早期痴呆症的检测和护理.
科学领域:
- 计算语言学和人工智能应用于医疗保健.
- 神经科学和认知科学研究神经退行性疾病.
背景情况:
- 痴呆症是一种渐进的认知衰退,影响日常活动和照顾需求.
- 早期发现痴呆症对于减少照顾者的负担和改善患者的治疗结果至关重要.
- 语音和语言困难被认为是痴呆症的早期指标.
研究的目的:
- 开发和评估一种使用语音数据评估认知衰退的方法.
- 专门评估语音相关性作为痴呆症记忆回忆的指标.
- 通过语言分析,利用深度学习模型来增强痴呆症检测.
主要方法:
- 这是一项采用深度学习转换器 (BERT,句子转换器) 的横截面,在线,自主管理的研究.
- 语音转录被编码为导出上下文描述的表示.
- 使用近位数相似度指标来测量发言句子的语义相关性,与皮特尸体痴呆症数据集相比较.
主要成果:
- 拟议的多QA-MPNet模型在捕获上下文和语义记忆方面表现出卓越的性能.
- 同位数相似性被确定为评估口语序列相关性的最有效措施.
- 该方法在分析早期痴呆症指标的语音方面显示出显著的潜力.
结论:
- 同位数相似度指标可以有效地识别口语中的语义相关性,作为痴呆症的早期预警信号.
- 语音分析,以及其他方式,如脑成像,可以帮助早期识别痴呆症.
- 开发的深度学习方法为改善痴呆症诊断准确性和效率提供了重要的临床影响.
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