title: Application of data mining techniques in pharmacovigilance.
abstract: OBJECTIVE
  To discuss the potential use of data mining and knowledge discovery in databases for detection of adverse drug events (ADE) in pharmacovigilance.
  
  
  METHODS
  A literature search was conducted to identify articles, which contained details of data mining, signal generation or knowledge discovery in relation to adverse drug reactions or pharmacovigilance in medical databases.
  
  
  RESULTS
  ADEs are common and result in significant mortality, and despite existing systems drugs have been withdrawn due to ADEs many years after licensing. Knowledge discovery in databases (KDD) is a technique which may be used to detect potential ADEs more efficiently. KDD involves the selection of data variables and databases, data preprocessing, data mining and data interpretation and utilization. Data mining encompasses a number of statistical techniques including cluster analysis, link analysis, deviation detection and disproportionality assessment which can be utilized to determine the presence of and to assess the strength of ADE signals. Currently the only data mining methods to be used in pharmacovigilance are those of disproportionality, such as the Proportional Reporting Ratio and Information Component, which have been used to analyse the UK Yellow Card Scheme spontaneous reporting database and the WHO Uppsala Monitoring Centre database. The association of pericarditis with practolol but not with other beta-blockers, the association of captopril and other angiotensin-converting enzymes with cough, and the association of terfenadine with heart rate and rhythm disorders could be identified by mining the WHO database.
  
  
  CONCLUSIONS
  In view of the importance of ADEs and the development of massive data storage systems and powerful computer systems, the use of data mining techniques in knowledge discovery in medical databases is likely to be of increasing importance in the process of pharmacovigilance as they are likely to be able to detect signals earlier than using current methods.
authors.affiliation: Division of Clinical Pharmacology, Department of Medicine, McMaster University, 105 Main Street East, Level P1, Hamilton, Ontario L8N 1G6, Canada.
authors.forename: Andrew M
authors.initials: AM
authors.lastname: Wilson
authors.forename: Lehana
authors.initials: L
authors.lastname: Thabane
authors.forename: Anne
authors.initials: A
authors.lastname: Holbrook
mesh_terms: D003625:Data Collection / Q000379:methods*; D016206:Databases, Bibliographic / Q000706:statistics & numerical data*; D016208:Databases, Factual / Q000706:statistics & numerical data*; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D016247:Information Storage and Retrieval / Q000706:statistics & numerical data*