A new AI assisted approach aligns data standards and accelerates interoperability in biomedical research.
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Long Rodney Alan, Ballard Shannon, Shah Syed, Bianchi Owen, Jones Lietsel, Koretsky Mathew J, Kuznetsov Nicole, Marsan Elise, Jen Bryant, Chiang Philip, Mukherjee Abhradeep, Blauwendraat Cornelis, Leonard Hampton, Vitale Dan, Levine Kristin, Bandres-Ciga Sara, Jarreau Paige, Brannelly Patrick, Phatak Mukta, Pantazis Caroline, Screven Laurel, Andersh Kate, Kapasi Alifiya, Crary John F, Gutman David, Dugger Brittany N, Biber Sarah, Hohman Timothy, Faghri Faraz, Griswold Michael, Sargent Lana, Keuren-Jensen Kendall van, Singleton Andrew B, Fann Yang, Nalls Mike A, Iwaki Hirotaka
Аннотация
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.