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Information Extraction From FDA Drug Labeling to Enhance Product-Specific Guidance Assessment Using Natural Language Processing
Journal article   Open access   Peer reviewed

Information Extraction From FDA Drug Labeling to Enhance Product-Specific Guidance Assessment Using Natural Language Processing

Yiwen Shi, Ping Ren, Yi Zhang, Xiajing Gong, Meng Hu and Hualou Liang
Frontiers in research metrics and analytics, v 6, pp 670006-670006
10 Jun 2021
DOI: https://doi.org/10.3389/frma.2021.670006
PMID: 34179681
url
https://www.frontiersin.org/articles/10.3389/frma.2021.670006/pdfView
Published, Version of Record (VoR) Open
url
https://doi.org/10.3389/frma.2021.670006View
Published, Version of Record (VoR) Open

Abstract

BERT FDA drug labels information extraction NLP product specific guidance Research Metrics and Analytics
Towards the objectives of the UnitedStates Food and Drug Administration (FDA) generic drug science and research program, it is of vital importance in developing product-specific guidances (PSGs) with recommendations that can facilitate and guide generic product development. To generate a PSG, the assessor needs to retrieve supportive information about the drug product of interest, including from the drug labeling, which contain comprehensive information about drug products and instructions to physicians on how to use the products for treatment. Currently, although there are many drug labeling data resources, none of them including those developed by the FDA (e.g., Drugs@FDA) can cover all the FDA-approved drug products. Furthermore, these resources, housed in various locations, are often in forms that are not compatible or interoperable with each other. Therefore, there is a great demand for retrieving useful information from a large number of textual documents from different data resources to support an effective PSG development. To meet the needs, we developed a Natural Language Processing (NLP) pipeline by integrating multiple disparate publicly available data resources to extract drug product information with minimal human intervention. We provided a case study for identifying food effect information to illustrate how a machine learning model is employed to achieve accurate paragraph labeling. We showed that the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model is able to outperform the traditional machine learning techniques, setting a new state-of-the-art for labelling food effect paragraphs from drug labeling and approved drug products datasets.

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18 citations in Scopus

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