一种基于本体学的方法,用于对阿尔茨海默病数据资源的协调和交叉队列查询
Xubing Hao1, Xiaojin Li2,3, Guo-Qiang Zhang1,2,3
1McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
BMC medical informatics and decision making
|August 4, 2023
概括
这项研究将NACC和ADNI的阿尔茨海默氏症 (AD) 数据与本体学相协调,从而实现跨队列查询. 开发的系统增强了AD研究的数据可查和可重复使用性.
科学领域:
- 神经科学是一个神经科学.
- 生物医学信息学 生物医学信息学
- 数据科学数据科学数据科学
背景情况:
- 国家阿尔茨海默病协调中心 (NACC) 和阿尔茨海默病神经成像计划 (ADNI) 是美国阿尔茨海默病 (AD) 研究数据的关键资源.
- 目前正在努力使NACC和ADNI数据公平 (可查找,可互操作,可访问,可重复使用),但跨资源协调和互操作性仍然有限.
研究的目的:
- 开发一种基于本体学的方法来协调NACC和ADNI之间的数据元素.
- 创建一个基于网络的系统,用于在NACC和ADNI资源中查询患者队列.
主要方法:
- 来自NACC和ADNI的数据元素被映射出来,它们的允许值得到了协调.
- 一个阿尔茨海默氏病数据元素本体 (ADEO) 被构建以建模协调数据.
- 开发了一种原型的交叉队列查询系统,具有Web界面,查询引擎和MongoDB后端.
主要成果:
- 在NACC和ADNI数据元素之间确定了172个映射.
- 使用这些映射构建了ADEO,创造了共同的概念.
- 协调过程导致了五个文件,详细说明了共同的概念,变量,映射和价值协调.
结论:
- 阿尔茨海默病数据元件本体 (ADEO) 促进了数据协调和NACC和ADNI的交叉队列查询.
- 开发的查询系统的框架是适应查询患者队列从多个异质数据源超越AD研究.
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