Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel
AskFDALabel exhibited high consistency with human AE annotation, particularly in classifying and profiling DILI and DICT. Thus, it can significantly enhance the efficiency and accuracy of AE annotation, with promising potential for advanced AE surveillance and drug safety research.
Authors
- Joshua Xu (ORCID: https://orcid.org/0000-0001-5313-5847)
- Weida Tong (ORCID: https://orcid.org/0000-0003-3488-6148)
- Leihong Wu (ORCID: https://orcid.org/0000-0002-4093-3708)
- Hong Fang (ORCID: https://orcid.org/0000-0002-2330-1469)
- Yanyan Qu (ORCID: https://orcid.org/0009-0005-3094-3098)
Institutions
- National Center for Toxicological Research (US)
Publication Details
- Journal
- Drug Safety
- Published
- 2025-02-20
- DOI
- https://doi.org/10.1007/s40264-025-01520-1
- Citations
- 9
- Primary Topic
- Computational Drug Discovery Methods
- Type
- article
- Field-Weighted Citation Impact
- 13.54