机器学习对患有大赦轻度认知障碍和非大赦轻度认知障碍的患者从书面图片描述任务的分类
Hana Kim1, Argye E Hillis2,3,4, Charalambos Themistocleous5
1Department of Communication Sciences and Disorders, University of South Florida, Tampa, FL 33620, USA.
Brain sciences
|July 27, 2024
概括
使用人工智能分析书面样本,可以准确地区分amnestic轻度认知障碍 (aMCI) 和非amnesticMCI (naMCI). 这种自然语言处理方法提供了一个具有成本效益的诊断工具,用于早期检测和管理认知衰退.
科学领域:
- 神经科学是一个神经科学.
- 计算语言学 计算语言学
- 老年学是一门学科.
背景情况:
- 轻度认知障碍 (MCI) 是正常衰老和痴呆之间的关键过渡阶段.
- MCI存在两种主要的临床变异:记忆障碍性MCI (aMCI) 和非记忆障碍性MCI (naMCI),它们具有明显的痴呆进展率.
- 准确分类aMCI和MAMCI是具有挑战性的,但在预后和治疗方面具有临床意义.
研究的目的:
- 研究从书面图片描述中进行话语水平语言分析的实用性,以对aMCI和MAMCI进行分类.
- 开发和验证一种生态和成本效益的方法来区分MCI亚型.
- 探索自然语言处理 (NLP) 和人工智能在分析认知评估的书面样本方面的潜力.
主要方法:
- 招募了169名被诊断患有aMCI或mMCI的人.
- 除了神经生理学评估,还进行了简短的书面图片描述任务.
- 采用自然语言处理 (NLP) 和BERT预训练语言模型来分析书面样本中的语言特征.
主要成果:
- 书面话语分析在aMCI和mMCI之间实现了高达90%的整体分类准确性.
- 人工智能驱动的分类模型在区分MCI亚型方面表现优于传统的认知措施.
- 书面样本中的语言模式有效地捕捉了aMCI和MAMCI特有的微妙的神经认知差异.
结论:
- 书面图片描述任务,当被AI分析时,提供了一个非常准确的方法来分类aMCI和MAMCI.
- 这种人工智能驱动的方法提供了一个有希望的,非侵入性的工具,以帮助诊断,预后和管理MCI.
- 对书面话语的自动化分析有潜力显著提高对认知障碍患者的早期发现和个性化护理.
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