在认知评估中的即时单词回忆可以使用机器学习技术预测痴呆症
Michael Adebisi Fayemiwo1,2,3, Toluwase Ayobami Olowookere4, Oluwabunmi Omobolanle Olaniyan1
1Department of Computer Science, Redeemer's University, Ede, Osun State, Nigeria.
Alzheimer's research & therapy
|June 15, 2023
概括
结合来自个人和代理的即时单词回忆反应,使用机器学习模型显著改善了痴呆症预测. 其他认知测试表现不佳,突出了这个特定的回忆任务的有效性.
科学领域:
- 老年学是一门学科.
- 认知科学 认知科学
- 人工智能的人工智能
背景情况:
- 痴呆症是一个日益严重的公共卫生问题,老年人群中患病率越来越高.
- 现有的机器学习 (ML) 模型用于痴呆症预测,通常达到高精度,但敏感性较低.
- 特定的认知评估数据的实用性,特别是单词回忆特征,用于使用ML预测痴呆症,仍未得到充分探索.
研究的目的:
- 研究单词回忆认知特征在开发痴呆症预测ML模型中的有效性.
- 评估ML模型在预测痴呆症方面的灵敏性表现.
- 为了确定样本人 (SP) 和代理反应的最佳组合,用于痴呆症预测.
主要方法:
- 通过使用来自国家健康和衰老趋势研究 (NHATS) 的数据进行了九项实验.
- 四个ML算法 (KNN,决策树,随机森林,ANN) 用于构建预测模型.
- 实验重点是"词延迟"",告诉你能回忆的词"和"立即回忆的词"任务,分析SP和代理响应.
主要成果:
- 使用联合SP和代理响应的"即时单词回忆"任务在所有四个ML模型中实现了1.00的完美灵敏度.
- "单词延迟"和"告诉你可以回忆的单词"任务显示出预测性能不佳.
- 在"word-delay"和"tell-words-you-can-recall"任务中将SP和代理响应结合起来,产生了0.60.0的最高灵敏度.
结论:
- 来自SP和代理的即时单词回忆任务中的响应组合对于痴呆症预测具有临床价值.
- 即时单词回忆认知评估是预测痴呆症的可靠方法.
- 根据这项研究的发现",词延迟"和"告诉你-你可以-回忆"任务对于痴呆症预测是不可靠的.
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