一个完全自动化的迷你精神状态考试评估模型,使用计算机算法进行认知查
Lihua Chen1, Meiwei Zhang2, Weihua Yu3
1Department of Geriatrics, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
Journal of Alzheimer's disease : JAD
|February 2, 2024
概括
一个新的自动化迷你心理状态检查 (MMSE) 工具准确地选了老年人的认知表现. 这种数字评估显示了与医生评级的高度一致性,为认知健康评估提供了一个有前途的解决方案.
科学领域:
- 老年学是指老年学的学科.
- 数字健康数字健康
- 认知科学 认知科学
背景情况:
- 全球人口老龄化增加了对有效认知评估工具的需求.
- 数字创新对于评估老年人的认知表现至关重要.
- 开发自动化系统可以简化认知查过程.
研究的目的:
- 创建一个完全自动化的迷你心理状态考试 (MMSE) 评估模型.
- 根据医生评估,验证自动MMSE评级的一致性和准确性.
主要方法:
- 开发了MMSE (AAM-MMSE) 的自动化评估模型,这是一个10分钟的计算机化工具,反映了传统的中国MMSE.
- 通过将其评级与427名参与者 (平均年龄60.6) 的医生评级进行比较,评估了AAM-MMSE的有效性.
主要成果:
- 在医生和AAM-MMSE之间发现了全尺度的高interrater可靠性 (ICC=0.952).
- 与医生分数相比,AAM-MMSE评级的准确性为87%,偏差为1.48分.
- 虽然与音频相关的项目显示了很高的一致性,但像"阅读和服从"这样的特定任务几乎没有公平的一致性.
结论:
- 对于MMSE的自动评估模型 (AAM-MMSE) 提供了一个有希望的,准确的,完全自动化的认知查系统.
- 这种数字工具可以帮助有效评估老年人群的认知功能.
- 进一步细化可能会改善自动化评估中的特定认知任务的协议.
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