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Nathan Pevy¹, Heidi Christensen², Traci Walker³

¹Sheffield Institute for Translational Neuroscience (SITraN), University of Sheffield, Sheffield, UK.

|December 13, 2023

概括

对口头描述的自动分析提高了暂时失去意识 (TLOC) 诊断的准确性. 将症状问卷与语言分析结合起来,可以更好地区分和功能性/离散性发作.

科学领域:

神经学神经学
人工智能的人工智能
计算语言学计算语言学

背景情况:

过渡性失去意识 (TLOC) 经常被误诊,导致适当的专业护理延迟.
当前的临床决策工具往往难以区分TLOC的主要原因:,功能/离散性 (FDS) 和昏迷.
以前的研究表明,发作与FDS发作的口头叙述中存在不同的语言模式.

研究的目的:

通过整合患者报告的症状和口头TLOC描述的自动分析来探索增强TLOC诊断的可行性.
开发一种临床决策工具,改进现有的方法来区分常见的TLOC原因,特别是和FDS.
评估结合症状问卷和来自口语TLOC叙述的语言特征的预测准确度.

主要方法:

参与者填写了一份在线问卷 (iPEP) 并与虚拟代理人 (VA) 进行互动,以提供他们TLOC事件的口头描述.
支持矢量机器 (SVM) 用于分类,利用来自iPEP的特征和三组语言特征:表述努力,语义词类别和语音部分 (动词,副词,形容词使用).
一个嵌套的离开一个-out交叉验证策略被用于训练和评估SVM模型.

主要成果:

仅仅使用 iPEP 问卷就能达到 65.8% 的诊断准确率.
纳入来自VA相互作用的语言特征显著提高了诊断准确度,达到85.5%.
改进后的模型显示了和FDS之间的区别显著改善,解决了以前工具的一个关键局限性.

结论:

自动分析口语TLOC描述,当通过在线应用程序和VA与症状数据集成时,可以显著提高诊断准确性.
这种方法为增强TLOC的临床决策支持工具提供了一个有希望的方法.
这些发现表明了改善临床分层的潜力,指导心脏病或神经病学调查和管理的适当转诊.

关键词:

分类分类分类分类.是一种病.功能性发作功能性发作自然语言处理自然语言处理.语音分析语音分析过渡性失去意识的时间.

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