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Evaluation of Natural Language Processing (NLP) systems to annotate drug product labeling with MedDRA terminology by Thomas Ly; Carol Pamer; Oanh Dang; Sonja Brajovic; Shahrukh Haider; Taxiarchis Botsis; David Milward; Andrew Winter; Susan Lu; Robert Ball is a Computer Science article available to read on EtoBox.

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## Introduction: The fda adverse event reporting system (faers) is a primary data source for identifying unlabeled adverse events (aes) in a drug or biologic drug product's postmarketing phase. many ae reports must be reviewed by drug safety experts to identify unlabeled aes, even if the reported aes are previously identified, labeled aes. integrating the labeling status of drug product aes into faers could increase report triage and review efficiency. medical dictionary for regulatory activities (meddra) is the standard for coding ae terms in faers cases. however, drug manufacturers are not required to use meddra to describe aes in product labels. we hypothesized that natural language processing (nlp) tools could assist in automating the extraction and meddra mapping of ae terms in drug product labels. ## Materials and methods: We evaluated the performance of three nlp systems, (ether, i2e, metamap) for their ability to extract ae terms from drug labels and translate the terms to meddra preferred terms (pts). pharmacovigilance-based annotation guidelines for extracting ae terms from drug labels were developed for this study. we compared each system's output to meddra pt ae lists,

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Author

Thomas Ly; Carol Pamer; Oanh Dang; Sonja Brajovic; Shahrukh Haider; Taxiarchis Botsis; David Milward; Andrew Winter; Susan Lu; Robert Ball

Publisher

Elsevier Science; Elsevier ; Elsevier Inc.; Elsevier BV (ISSN 1532-0464)

Published

2018

Language

EN

Field

Computer Science (Physical Sciences)