UMMS Affiliation

Department of Quantitative Health Sciences; Department of Medicine

Publication Date

2014-06-27

Document Type

Article

Disciplines

Artificial Intelligence and Robotics | Health and Medical Administration | Health Communication | Health Information Technology | Health Services Research | Translational Medical Research

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. C

ONCLUSIONS: 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.

Keywords

adverse drug events, natural language processing, pharmacovigilance, UMCCTS funding

Rights and Permissions

© Balaji Polepalli Ramesh, Steven M Belknap, Zuofeng Li, Nadya Frid, Dennis P West, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 27.06.2014. This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

DOI of Published Version

10.2196/medinform.3022

Source

JMIR Med Inform. 2014 Jun 27;2(1):e10. doi: 10.2196/medinform.3022. Link to article on publisher's site

Journal/Book/Conference Title

JMIR medical informatics

Related Resources

Link to Article in PubMed

PubMed ID

25600332

Share

COinS
 
 

To view the content in your browser, please download Adobe Reader or, alternately,
you may Download the file to your hard drive.

NOTE: The latest versions of Adobe Reader do not support viewing PDF files within Firefox on Mac OS and if you are using a modern (Intel) Mac, there is no official plugin for viewing PDF files within the browser window.