Literature DB >> 22195210

A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from PubMed citations.

Wei Wang1, Krystl Haerian, Hojjat Salmasian, Rave Harpaz, Herbert Chase, Carol Friedman.   

Abstract

Adverse drug events (ADEs) create a serious problem causing substantial harm to patients. An executable standardized knowledgebase of drug-ADE relations which is publicly available would be valuable so that it could be used for ADE detection. The literature is an important source that could be used to generate a knowledgebase of drug-ADE pairs. In this paper, we report on a method that automatically determines whether a specific adverse event (AE) is caused by a specific drug based on the content of PubMed citations. A drug-ADE classification method was initially developed to detect neutropenia based on a pre-selected set of drugs. This method was then applied to a different set of 76 drugs to determine if they caused neutropenia. For further proof of concept this method was applied to 48 drugs to determine whether they caused another AE, myocardial infarction. Results showed that AUROC was 0.93 and 0.86 respectively.

Entities:  

Mesh：

Year:  2011        PMID： 22195210      PMCID： PMC3243206     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN： 1559-4076


  7 in total

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Journal:  Pharmacoepidemiol Drug Saf       Date:  2009-12       Impact factor: 2.890

6.  A Bayesian neural network method for adverse drug reaction signal generation.

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Review 7.  Systematic review: agranulocytosis induced by nonchemotherapy drugs.

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  7 in total
  21 in total

1.  Text mining for adverse drug events: the promise, challenges, and state of the art.

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2.  Published cases of adverse drug reactions: has the quality of reporting improved over time?

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Review 3.  Crowdsourcing in biomedicine: challenges and opportunities.

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5.  Automatic adverse drug events detection using letters to the editor.

Authors:  Chao Yang; Padmini Srinivasan; Philip M Polgreen
Journal:  AMIA Annu Symp Proc       Date:  2012-11-03

6.  Lessons learned from developing a drug evidence base to support pharmacovigilance.

Authors:  J C Smith; J C Denny; Q Chen; H Nian; A Spickard; S T Rosenbloom; R A Miller
Journal:  Appl Clin Inform       Date:  2013-12-18       Impact factor: 2.342

7.  Towards Large-scale Twitter Mining for Drug-related Adverse Events.

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9.  Automated Determination of Publications Related to Adverse Drug Reactions in PubMed.

Authors:  Hayden Adams; Carol Friedman; Joseph Finkelstein
Journal:  AMIA Jt Summits Transl Sci Proc       Date:  2015-03-25

10.  Pharmacovigilance from social media: mining adverse drug reaction mentions using sequence labeling with word embedding cluster features.

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