绘制与阿尔茨海默病相关的数据元素和NIH常用数据元素的映射
Xubing Hao1, Rashmie Abeysinghe2, Fengbo Zheng1
1McWilliams School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA.
BMC medical informatics and decision making
|April 19, 2024
概括
使用计算模型在NACC,ADNI和NIH CDE等资源之间绘制阿尔茨海默病数据元素的映射显示出希望. 共同数据元素的标准化对于推进阿尔茨海默病研究至关重要.
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:
- 阿尔茨海默病 (AD) 研究依赖于来自NACC,ADNI和NIH CDE等资源的共享数据.
- 提高数据互操作性是促进AD理解,诊断和治疗的关键.
研究的目的:
- 评估与AD相关的数据元素在NACC,ADNI和NIH CDE之间的互操作性.
- 评估用于在这些资源之间映射数据元素的计算方法.
主要方法:
- 使用词包和词嵌入模型 (Word2Vec,BioWordVec) 来映射数据元素.
- 在NACC-ADNI,NACC-NIH CDE和ADNI-NIH CDE之间比较绘制性能.
主要成果:
- 词包获得了最高的精度; BioWordVec获得了最高的回忆.
- 映射率各不相同:15.92% (NACC-ADNI),9.74% (NACC-NIH CDE) 和2.25% (ADNI-NIH CDE) 进行映射.
结论:
- 计算绘图方法显示了链接AD数据的潜力.
- 在AD研究资源中标准化通用数据元素 (CDE) 对于最大化发现至关重要.
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