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Mapping of Alzheimer's disease related data elements and the NIH Common Data Elements.
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
Summary
Mapping Alzheimer's Disease data elements between resources like NACC, ADNI, and NIH CDE using computational models shows promise. Standardization of common data elements is crucial for advancing Alzheimer's research.
Area of Science:
- Neuroscience
- Biomedical Informatics
- Data Science
Background:
- Alzheimer's Disease (AD) research relies on shared data from resources like NACC, ADNI, and NIH CDE.
- Improving data interoperability is key to advancing AD understanding, diagnosis, and treatment.
Purpose of the Study:
- To assess the interoperability of AD-related data elements across NACC, ADNI, and NIH CDE.
- To evaluate computational approaches for mapping data elements between these resources.
Main Methods:
- Utilized bag-of-words and word embedding models (Word2Vec, BioWordVec) to map data elements.
- Compared mapping performance between NACC-ADNI, NACC-NIH CDE, and ADNI-NIH CDE.
Main Results:
- Bag-of-words achieved highest precision; BioWordVec achieved highest recall.
- Mapping rates varied: 15.92% (NACC-ADNI), 9.74% (NACC-NIH CDE), 2.25% (ADNI-NIH CDE).
Conclusions:
- Computational mapping approaches show potential for linking AD data.
- Standardizing Common Data Elements (CDEs) across AD research resources is essential for maximizing discoveries.
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