Semantic Harmonization of Alzheimer's Disease Datasets Using AD-Mapper.
Wegner P., Balabin H., Ay MC., Bauermeister S., Killin L., Gallacher J., Hofmann-Apitius M., Salimi Y., Alzheimer’s Disease Neuroimaging Initiative None., Japanese Alzheimer’s Disease Neuroimaging Initiative None., Aging Brain: Vasculature, Ischemia, and Behavior Study None., Alzheimer’s Disease Repository Without Borders Investigators None., European Prevention of Alzheimer’s Disease (EPAD) Consortium None.
BACKGROUND: Despite numerous past endeavors for the semantic harmonization of Alzheimer's disease (AD) cohort studies, an automatic tool has yet to be developed. OBJECTIVE: As cohort studies form the basis of data-driven analysis, harmonizing them is crucial for cross-cohort analysis. We aimed to accelerate this task by constructing an automatic harmonization tool. METHODS: We created a common data model (CDM) through cross-mapping data from 20 cohorts, three CDMs, and ontology terms, which was then used to fine-tune a BioBERT model. Finally, we evaluated the model using three previously unseen cohorts and compared its performance to a string-matching baseline model. RESULTS: Here, we present our AD-Mapper interface for automatic harmonization of AD cohort studies, which outperformed a string-matching baseline on previously unseen cohort studies. We showcase our CDM comprising 1218 unique variables. CONCLUSION: AD-Mapper leverages semantic similarities in naming conventions across cohorts to improve mapping performance.