Mapping vaccine names in clinical trials to vaccine ontology using cascaded fine-tuned domain-specific language models
This study provides a detailed insight into a cascaded framework of fine-tuned domain-specific language models improving mapping of VO from clinical trials. By effectively leveraging domain-specific information and applying weighted rule-based ensembles of different pre-trained BERT models, our framework can significantly enhance the mapping of VO from clinical trials.
Authors
- Jinjing Guo (ORCID: https://orcid.org/0000-0003-1590-8014)
- Zenan Sun (ORCID: https://orcid.org/0000-0003-1577-874X)
- Yongqun He (ORCID: https://orcid.org/0000-0001-9189-9661)
- Cui Tao (ORCID: https://orcid.org/0000-0002-4267-1924)
- Yuanyi Pan (ORCID: https://orcid.org/0000-0002-0595-4335)
- Fang Li (ORCID: https://orcid.org/0000-0001-8865-7717)
- Jianfu Li (ORCID: https://orcid.org/0000-0002-9949-7007)
- Yiming Li (ORCID: https://orcid.org/0009-0009-8784-1745)
Institutions
- University of Michigan (US)
- Jacksonville College (US)
- Mayo Clinic in Florida (US)
- The University of Texas Health Science Center at Houston (US)
Publication Details
- Journal
- Journal of Biomedical Semantics
- Published
- 2024-08-09
- DOI
- https://doi.org/10.1186/s13326-024-00318-x
- Citations
- 22
- Primary Topic
- Biomedical Text Mining and Ontologies
- Type
- article
- Field-Weighted Citation Impact
- 5.14
Funders
- National Institutes of Health