title: Novel data-mining methodologies for adverse drug event discovery and analysis.
abstract: An important goal of the health system is to identify new adverse drug events (ADEs) in the postapproval period. Datamining methods that can transform data into meaningful knowledge to inform patient safety have proven essential for this purpose. New opportunities have emerged to harness data sources that have not been used within the traditional framework. This article provides an overview of recent methodological innovations and data sources used to support ADE discovery and analysis.
authors.affiliation: Department of Biomedical Informatics, Columbia University Medical Center, New York, New York, USA. rave.harpaz@dbmi.columbia.edu
authors.forename: R
authors.initials: R
authors.lastname: Harpaz
authors.forename: W
authors.initials: W
authors.lastname: DuMouchel
authors.forename: N H
authors.initials: NH
authors.lastname: Shah
authors.forename: D
authors.initials: D
authors.lastname: Madigan
authors.forename: P
authors.initials: P
authors.lastname: Ryan
authors.forename: C
authors.initials: C
authors.lastname: Friedman
mesh_terms: D001185:Artificial Intelligence; D016022:Case-Control Studies; D015331:Cohort Studies; D057225:Data Mining / Q000379:methods*; D016206:Databases, Bibliographic; D016208:Databases, Factual; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D006801:Humans; D015999:Multivariate Analysis; D060735:Pharmacovigilance; D012107:Research Design