ArticleJMIR medical informatics2014
Automatically Recognizing Medication and Adverse Event Information From Food and Drug Administration's Adverse Event Reporting System Narratives.
Article in JMIR medical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
17 citing papers in PubMed.
- Pharmacovigilance insights: safety profiles of antifungal agents for invasive aspergillosis.Frontiers in pharmacology · 2025Article
- Generalizability of machine learning methods in detecting adverse drug events from clinical narratives in electronic medical records.Frontiers in pharmacology · 2023Article
- Automatic Detection of Adverse Drug Events in Geriatric Care: Study Proposal.JMIR research protocols · 2022Article
- Artificial Intelligent Context-Aware Machine-Learning Tool to Detect Adverse Drug Events from Social Media Platforms.Journal of medical toxicology : official journal of the American College of Medical Toxicology · 2022Article
- Language-agnostic pharmacovigilant text mining to elicit side effects from clinical notes and hospital medication records.Basic & clinical pharmacology & toxicology · 2022Article
- Application of Augmented Intelligence for Pharmacovigilance Case Seriousness Determination.Drug safety · 2020Article
- Towards Personalized Antithrombotic Treatments: Focus on P2YClinical pharmacokinetics · 2019Review
- Predicting life expectancy with a long short-term memory recurrent neural network using electronic medical records.BMC medical informatics and decision making · 2019Article
- Predicting the occurrence of surgical site infections using text mining and machine learning.PloS one · 2019Article
- Towards Drug Safety Surveillance and Pharmacovigilance: Current Progress in Detecting Medication and Adverse Drug Events from Electronic Health Records.Drug safety · 2019Article
- Adverse Drug Event Detection from Electronic Health Records Using Hierarchical Recurrent Neural Networks with Dual-Level Embedding.Drug safety · 2019Article
- Extraction of Information Related to Adverse Drug Events from Electronic Health Record Notes: Design of an End-to-End Model Based on Deep Learning.JMIR medical informatics · 2018Article
- Clinical Relation Extraction Toward Drug Safety Surveillance Using Electronic Health Record Narratives: Classical Learning Versus Deep Learning.JMIR public health and surveillance · 2018Article
- Training Augmented Intelligent Capabilities for Pharmacovigilance: Applying Deep-learning Approaches to Individual Case Safety Report Processing.Pharmaceutical medicine · 2018Article
- TwiMed: Twitter and PubMed Comparable Corpus of Drugs, Diseases, Symptoms, and Their Relations.JMIR public health and surveillance · 2017Article
- Bidirectional RNN for Medical Event Detection in Electronic Health Records.Proceedings of the conference. Association for Computational Linguistics. North American Chapter. Meeting · 2016Article
- Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.Journal of the American Medical Informatics Association : JAMIA · 2015Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundThe 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.
objectiveThe 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.
methodsWe 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.
resultsThe 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.
conclusionsIn 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.
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Registered trials
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