Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration's Adverse Event Reporting System Narratives

• Published in: JMIR medical informatics 2014-01, Vol.2 (1), p.e10-e10
• Main Authors: Polepalli Ramesh, Balaji, Belknap, Steven M, Li, Zuofeng, Frid, Nadya, West, Dennis P, Yu, Hong
• Format: Article
• Language: English
• Subjects: Annotations; Causality; Detection; Drugs; Identification; Injuries; Learning styles; Machine learning; Medical records; Narratives; Original Paper; Patients; Pharmaceuticals; Prescription drugs; Product safety; Semantics; Severity; Surveillance
• ISSN: 2291-9694; 2291-9694; 1438-8871
• DOI: 10.2196/medinform.3022

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. 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. 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. 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. 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.