认知中风:在超急性中风单元上的自动认知和情绪评估
Simon M Bell1,2,3,4, Bahman Mirheidari5, Kirsty A C Harkness2,3,4
1Sheffield Institute for Translational Neuroscience, School of Medicine and Population Health, University of Sheffield, 385a Glossop Rd, Broomhall, Sheffield S10 2HQ, UK.
Healthcare (Basel, Switzerland)
|November 27, 2025
概括
一个自动化工具CognoStroke使用人工智能驱动的语音分析评估中风幸存者的认知和情绪障碍. 它显示出在超急性中风单位有效查的前景,尽管存在一些与患者有关的挑战.
科学领域:
- 神经学 神经学
- 人工智能的人工智能
- 精神病学是一个精神病学.
背景情况:
- 在中风幸存者中,认知和情绪障碍很普遍,对结果和生活质量产生负面影响.
- 目前的评估方法对于超急性中风单位 (HASU) 是耗时且不切实际的.
- 需要自动化评估工具,以便在HASU设置中进行有效和及时的评估.
研究的目的:
- 在HASU中引入和评估CognoStroke,这是一个用于评估中风幸存者的认知和情绪障碍的自动化系统.
- 确定大型语言模型在分析语音情绪和认知状态分类方面的有效性.
- 确定在HASU中采用自动化评估工具的障碍.
主要方法:
- 通过大型语言模型 (GPT2,BART,RoBERTa),CognoStroke利用对话式AI和语音分析来分类认知 (MoCA) 和情绪 (GAD-7,PHQ-9) 值.
- 使用宏观F1分数 (MFSs) 来测量性能.
- 收集了患者报告的使用CognoStroke的障碍.
主要成果:
- 使用优化的提示组合,CognoStroke在MoCA值方面实现了0.783的最佳MFS.
- 获得PHQ-9的MFS为0.686,GAD-7的MFS为0.617,使用更少的提示得到了更好的得分.
- 在151名中风幸存者中,有75人完成了评估,其中包括计算机访问和技术焦虑等共同障碍.
结论:
- CognoStroke展示了基于认知和情绪状态对中风幸存者进行分类的潜力,有助于早期干预.
- 优化对话提示可以提高自动评估的准确性.
- 解决患者电脑访问和技术焦虑等挑战对于广泛实施至关重要.
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