A natural language processing approach to support biomedical data harmonization: Leveraging large language models
NLP techniques (especially LLMs), combined with ensemble learning, hold great potential in automating variable matching and accelerating biomedical data harmonization.
Authors
- Zachary Popp (ORCID: https://orcid.org/0009-0001-7935-5300)
- Rhoda Au (ORCID: https://orcid.org/0000-0001-7742-4491)
- Ting Fang Alvin Ang (ORCID: https://orcid.org/0000-0003-4687-2552)
- Jinying Chen (ORCID: https://orcid.org/0000-0001-7259-4301)
- Shubhi Jain (ORCID: https://orcid.org/0000-0002-8653-1552)
- Zexu Li (ORCID: https://orcid.org/0000-0003-2487-2447)
- S. Prabhu
- Vijetha Balakundi
Institutions
- Boston University (US)
- University of Massachusetts Boston (US)
- Framingham Heart Study (US)
Publication Details
- Journal
- PLoS ONE
- Published
- 2025-07-24
- DOI
- https://doi.org/10.1371/journal.pone.0328262
- Citations
- 4
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 7.44
Funders
- American Heart Association
- National Institute on Aging