Evaluation of a natural language processing tool for extracting gender, weight, ethnicity, and race in the US food and drug administration adverse event reporting system

Evaluation of a natural language processing tool for extracting gender, weight, ethnicity, and race in the US food and drug administration adverse event reporting system

The US Food and Drug Administration Adverse Event Reporting System (FAERS) contains over 24 million individual case safety reports (ICSRs). In this research project, we evaluated a natural language processing (NLP) tool's ability to extract four demographic variables (gender, weight, ethnicity,...

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Main Authors: Vivian Dang (Author), Eileen Wu (Author), Cindy M. Kortepeter (Author), Michael Phan (Author), Rongmei Zhang (Author), Yong Ma (Author), Monica A. Muñoz (Author)
Format: Book
Published: Frontiers Media S.A., 2022-11-01T00:00:00Z.
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