为成功的认知衰老评估可计算的表型
Glenn Smith1, Amber Miller2, David E Marra3
1Department of Clinical and Health Psychology, University of Florida, Gainesville.
Mayo Clinic proceedings. Innovations, quality & outcomes
|June 12, 2023
概括
一个可计算的表型算法成功地使用电子健康记录识别了成功的认知衰老的个体. 这种信息化方法有助于招聘关于衰老的前性研究.
科学领域:
- 老年学和生物信息学
- 计算流行病学计算流行病学
- 临床信息学 临床信息学
背景情况:
- 为成功的衰老研究招募参与者是一项挑战.
- 电子健康记录 (EHR) 包含有价值的数据,用于识别老龄化人口.
- 开发可计算的表型对于有效的参与者招聘至关重要.
研究的目的:
- 建立,应用和评估可计算的表型,用于招募具有成功认知衰老的个体.
- 为了确定可用EHR的变量,表明老年人成功衰老.
- 创建一个基于规则的算法来识别85岁及以上的符合条件的参与者.
主要方法:
- 对10位老龄化专家进行了采访,以确定关键变量.
- 开发了一种基于17个标准规则的可计算的表型算法.
- 将算法应用于佛罗里达大学健康电子记录数据库中的24,024名85岁以上的个人.
- 评估了同意参与者的认知和功能状态.
主要成果:
- 可计算的表型在EHR数据库中识别了45%的85岁以上符合条件的个人.
- 大约4%的确定的个人回应了研究公告.
- 218名参与者 (65%的同意者) 在评估时满足了成功的认知衰老标准.
结论:
- 这项研究证明了可计算的表型在成功的衰老研究中招募参与者的有效性.
- 这种方法提供了使用大数据和信息学在招募参与者前性队列研究的概念证明.
- 开发的算法可以促进大规模识别符合特定衰老标准的个体.
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