title: A drug-adverse event extraction algorithm to support pharmacovigilance knowledge mining from PubMed citations.
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.
authors.affiliation: Dept of Biomedical Informatics, Columbia University, New York, NY, USA.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.forename: Krystl
authors.initials: K
authors.lastname: Haerian
authors.forename: Hojjat
authors.initials: H
authors.lastname: Salmasian
authors.forename: Rave
authors.initials: R
authors.lastname: Harpaz
authors.forename: Herbert
authors.initials: H
authors.lastname: Chase
authors.forename: Carol
authors.initials: C
authors.lastname: Friedman
mesh_terms: D000465:Algorithms; D057225:Data Mining / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D051188:Knowledge Bases; D009203:Myocardial Infarction / Q000139:chemically induced; D009503:Neutropenia / Q000139:chemically induced; D060735:Pharmacovigilance; D039781:PubMed