AI Article Synopsis
- Alzheimer's Disease (AD) is a serious condition affecting memory and cognitive functions, with significant research underway in the U.S. using resources like NACC, ADNI, and NIH CDE for data sharing and collaboration.
- To understand how data elements from these resources can work together, researchers applied various models, including bag-of-words and Word2Vec-based methods, to map data elements between NACC, ADNI, and NIH CDE.
- Results from the mapping showed that while the bag-of-words approach had the highest precision, the BioWordVec method had the best recall; overall, there’s a need for better standardization of common data elements to enhance research efforts.
Article Abstract
Background: Alzheimer's Disease (AD) is a devastating disease that destroys memory and other cognitive functions. There has been an increasing research effort to prevent and treat AD. In the US, two major data sharing resources for AD research are the National Alzheimer's Coordinating Center (NACC) and the Alzheimer's Disease Neuroimaging Initiative (ADNI); Additionally, the National Institutes of Health (NIH) Common Data Elements (CDE) Repository has been developed to facilitate data sharing and improve the interoperability among data sets in various disease research areas.
Method: To better understand how AD-related data elements in these resources are interoperable with each other, we leverage different representation models to map data elements from different resources: NACC to ADNI, NACC to NIH CDE, and ADNI to NIH CDE. We explore bag-of-words based and word embeddings based models (Word2Vec and BioWordVec) to perform the data element mappings in these resources.
Results: The data dictionaries downloaded on November 23, 2021 contain 1,195 data elements in NACC, 13,918 in ADNI, and 27,213 in NIH CDE Repository. Data element preprocessing reduced the numbers of NACC and ADNI data elements for mapping to 1,099 and 7,584 respectively. Manual evaluation of the mapping results showed that the bag-of-words based approach achieved the best precision, while the BioWordVec based approach attained the best recall. In total, the three approaches mapped 175 out of 1,099 (15.92%) NACC data elements to ADNI; 107 out of 1,099 (9.74%) NACC data elements to NIH CDE; and 171 out of 7,584 (2.25%) ADNI data elements to NIH CDE.
Conclusions: The bag-of-words based and word embeddings based approaches showed promise in mapping AD-related data elements between different resources. Although the mapping approaches need further improvement, our result indicates that there is a critical need to standardize CDEs across these valuable AD research resources in order to maximize the discoveries regarding AD pathophysiology, diagnosis, and treatment that can be gleaned from them.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11027215 | PMC |
| http://dx.doi.org/10.1186/s12911-024-02500-8 | DOI Listing |
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