Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration’s Adverse Event Reporting System Narratives
Open Access
- 27 June 2014
- journal article
- Published by JMIR Publications Inc. in JMIR Public Health and Surveillance
- Vol. 2 (1), e10
- https://doi.org/10.2196/medinform.3022
Abstract
Background: The Food and Drug Administration’s (FDA) Adverse Event Reporting System (FAERS) is a repository of spontaneously-reported adverse drug events (ADEs) for FDA-approved prescription drugs. FAERS reports include both structured reports and unstructured narratives. The narratives often include essential information for evaluation of the severity, causality, and description of ADEs that are not present in the structured data. The timely identification of unknown toxicities of prescription drugs is an important, unsolved problem. Objective: The objective of this study was to develop an annotated corpus of FAERS narratives and biomedical named entity tagger to automatically identify ADE related information in the FAERS narratives. Methods: We developed an annotation guideline and annotate medication information and adverse event related entities on 122 FAERS narratives comprising approximately 23,000 word tokens. A named entity tagger using supervised machine learning approaches was built for detecting medication information and adverse event entities using various categories of features. Results: The annotated corpus had an agreement of over .9 Cohen’s kappa for medication and adverse event entities. The best performing tagger achieves an overall performance of 0.73 F1 score for detection of medication, adverse event and other named entities. Conclusions: In this study, we developed an annotated corpus of FAERS narratives and machine learning based models for automatically extracting medication and adverse event information from the FAERS narratives. Our study is an important step towards enriching the FAERS data for postmarketing pharmacovigilance. [JMIR Med Inform 2014;2(1):e10]This publication has 71 references indexed in Scilit:
- Novel Data-Mining Methodologies for Adverse Drug Event Discovery and AnalysisClinical Pharmacology & Therapeutics, 2012
- Data-Driven Prediction of Drug Effects and InteractionsScience Translational Medicine, 2012
- Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysisJournal of the American Medical Informatics Association, 2011
- Using information mining of the medical literature to improve drug safetyJournal of the American Medical Informatics Association, 2011
- Comparison of computerized surveillance and manual chart review for adverse eventsJournal of the American Medical Informatics Association, 2011
- Detecting hedge cues and their scope in biomedical text with conditional random fieldsJournal of Biomedical Informatics, 2010
- Biclustering of Adverse Drug Events in the FDA's Spontaneous Reporting SystemClinical Pharmacology & Therapeutics, 2010
- Biomedical negation scope detection with conditional random fieldsJournal of the American Medical Informatics Association, 2010
- Lancet: a high precision medication event extraction system for clinical textJournal of the American Medical Informatics Association, 2010
- Extracting medication information from clinical textJournal of the American Medical Informatics Association, 2010