CDEMapper: enhancing National Institutes of Health common data element use with large language models
This work opens up the potential of using LLMs to assist with CDE mapping when aligning local data elements with NIH CDEs. Additionally, this effort helps researchers better understand the gaps between their data elements and NIH CDEs while promoting CDE reusability.
Authors
- Lingfei Qian (ORCID: https://orcid.org/0000-0001-6406-7069)
- Yan Wang (ORCID: https://orcid.org/0000-0003-1036-9365)
- Pritham Ram
- Xubing Hao (ORCID: https://orcid.org/0000-0003-2207-4097)
- Jimin Huang (ORCID: https://orcid.org/0000-0002-3501-3907)
- Na Hong (ORCID: https://orcid.org/0000-0001-6798-1761)
- Licong Cui (ORCID: https://orcid.org/0000-0001-5549-8780)
- Xiaoqian Jiang (ORCID: https://orcid.org/0000-0001-9933-2205)
- Ruey-Ling Weng (ORCID: https://orcid.org/0009-0005-4003-7339)
- Vincent Zhang (ORCID: https://orcid.org/0009-0002-3931-7374)
- Hua Xu (ORCID: https://orcid.org/0000-0002-5274-4672)
- Huan He (ORCID: https://orcid.org/0000-0002-6032-3392)
- Yan Hu (ORCID: https://orcid.org/0009-0008-2413-5918)
- Yujia Zhou (ORCID: https://orcid.org/0000-0002-3716-7854)
- Qianqian Xie (ORCID: https://orcid.org/0000-0002-9588-7454)
- Fongci Lin
Institutions
- Yale University (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Journal of the American Medical Informatics Association
- Published
- 2025-04-15
- DOI
- https://doi.org/10.1093/jamia/ocaf064
- Citations
- 2
- Primary Topic
- Data Quality and Management
- Type
- article
- Field-Weighted Citation Impact
- 4.21
Funders
- National Institutes of Health
- U.S. National Library of Medicine