发展阿尔茨海默病风险评分未来的初级保健:一个白盒方法
Yumiko Wiranto1, Devin R Setiawan2, Amber Watts1,3
1Department of Psychology, University of Kansas, Lawrence, Kansas, USA.
medRxiv : the preprint server for health sciences
|December 3, 2025
概括
一个新的可解释的评分系统,FLAME评分卡,使用初级保健数据预测阿尔茨海默病 (AD) 风险. 该工具通过评估功能,语言,记忆和执行功能来帮助早期干预.
科学领域:
- 神经科学是一个神经科学.
- 老年学是指老年学的学科.
- 主要护理医学 医疗保健
背景情况:
- 阿尔茨海默氏病 (AD) 诊断是复杂的,经常延迟,阻碍了早期干预.
- 需要可解释的评分系统来弥合诊断复杂性和非专业环境中的及时干预之间的差距.
研究的目的:
- 开发一个风险评分来预测阿尔茨海默病 (AD) 的可能性.
- 使用可解释的机器学习与可在初级保健环境中访问的变量.
主要方法:
- 阿尔茨海默病神经成像计划 (ADNI) 和国家阿尔茨海默病协调中心 (NACC) 队列的二次数据分析.
- 使用FasterRisk算法开发了FLAME (功能,语言,记忆,执行功能) 记分卡.
- 通过曲线下的面积 (AUC) 度量来验证模型的性能.
主要成果:
- 在ADNI队列中,FLAME得分表显示出强大的预测性表现 (AUC = 0.8680.892).
- 在NACC队列中验证时,该模型保持了强大的性能 (AUC = 0.795).
- 关键预测因素包括动物命名,Trail Making Test B,逻辑记忆延迟回忆和功能评估问卷 (FAQ) 成绩.
结论:
- 这些发现表明,使用现有临床数据预测AD发展的潜力.
- FLAME得分卡适用于综合初级保健机构,促进早期AD检测和干预.
- 这种方法为开发其他类型痴呆症类似工具提供了基础.
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