A new AI assisted approach aligns data standards and accelerates interoperability in biomedical research.
A new AI assisted approach aligns data standards and accelerates interoperability in biomedical research.
Where did the research take place?
The study site has not been established. Author addresses may differ from where the research occurred.
Bethesda, US · Author affiliation
Center for Alzheimer's and Related Dementias, National Institute on Aging, National Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD, USA.Location evidence
US · Author affiliation · country only
DataTecnica LLC, Washington, DC, USA.Location evidence
Oakland, US · Author affiliation
10,000 Brains Project., Oakland, CA, USA.Location evidence
Chicago, US · Author affiliation
Rush University Medical Center, Chicago, IL, USA.Location evidence
New York City, US · Author affiliation
Departments of Pathology, Neuroscience, and Artificial Intelligence & Human Health, Neuropathology Brain Bank & Research CoRE, Ronald M. Loeb Center for Alzheimer's Disease, Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA.Location evidence
Atlanta, US · Author affiliation
Emory University, Atlanta, GA, USA.Location evidence
Davis, US · Author affiliation
University of California - Davis, Sacramento, CA, USA.Location evidence
Sacramento, US · Author affiliation
University of California - Davis, Sacramento, CA, USA.Location evidence
Seattle, US · Author affiliation
University of Washington, Seattle, WA, USA.Location evidence
Nashville, US · Author affiliation
Vanderbilt University, Nashville, TN, USA.Location evidence
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Original abstract
We demonstrate how Large Language Models (LLMs) accelerate biomedical data harmonization through automated Common Data Element (CDE) generation. We processed 31 datasets including clinical taxonomies and research data dictionaries through OpenAI's Generative Pre-trained Transformer - 4 (API Model gpt-4-0613), generating comprehensive metadata for each element using a template-based system. Subject-matter experts validated outputs, finding 94% of generated metadata fields required no revision overall, with an unweighted accuracy of 83.8%, unweighted, for semi-structured sources. Dramatically faster than manual approaches. Our system uses ElasticSearch with weighted field matching to identify semantic equivalences between variables, avoiding duplicate CDEs while building a standardized repository. Testing with Alzheimer's Disease Neuroimaging Initiative (ADNI) and Global Parkinson's Genetic Program (GP2) datasets showed 32.4% of previously unseen headers successfully mapped to our CDEs, with interoperability scores averaging 53.8/100 based on matching, completeness, and compliance metrics. This approach automates the most tedious aspects of data integration, reducing barriers to cross-study collaboration in biomedical research.