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.

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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
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article

Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel

Joshua Xu, Weida Tong, Leihong Wu, Hong Fang et al.
9 citations
Drug Safety
Computational Drug Discovery Methods
13.54
article

Leveraging FDA Labeling Documents and Large Language Model to Enhance Annotation, Profiling, and Classification of Drug Adverse Events with AskFDALabel

Joshua Xu, Weida Tong, Leihong Wu, Hong Fang, Yanyan Qu
article en
9 citations

Abstract

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.

Drug SafetyVol. 48(6)
National Center for Toxicological Research (US)
Openalex Percentile: Top 1%
Computational Drug Discovery Methods
13.54
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