Improved methods for ADE detection and analysis may identify novel drug safety signals and lead to improved methods for avoiding ADEs, with their attendant burden of morbidity, mortality, and cost. In the United States alone, ADEs account for more than 770,000 injuries and deaths annually, and an increased average length of stay in hospitals at a cost of between $1.56 and $5.60 billion annually. Studies have reported that ADEs account for nearly 20% of all adverse events that occur in hospitalized patients. An adverse drug event (ADE) is an injury resulting from a medical intervention related to a drug, including harm caused by the drug (adverse drug reactions and overdoses), and harm from the use of the drug (including dose reductions and discontinuations of drug therapy). Our study is an important step towards enriching the FAERS data for postmarketing pharmacovigilance.Īn adverse event (AE) is an injury or untoward medical occurrence to a patient or clinical investigation subject who has been administered a pharmaceutical product and the AE does not necessarily have a causal relationship with the administered treatment. The best performing tagger achieves an overall performance of 0.73 F1 score for detection of medication, adverse event and other named entities.Ĭonclusions: 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.

9 Cohen’s kappa for medication and adverse event entities. Results: The annotated corpus had an agreement of over.

A named entity tagger using supervised machine learning approaches was built for detecting medication information and adverse event entities using various categories of features. 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. 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. The timely identification of unknown toxicities of prescription drugs is an important, unsolved problem. The narratives often include essential information for evaluation of the severity, causality, and description of ADEs that are not present in the structured data. FAERS reports include both structured reports and unstructured narratives. University of Massachusetts Medical SchoolĮmail: 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.

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