title: Data-mining analyses of pharmacovigilance signals in relation to relevant comparison drugs.
abstract: OBJECTIVE
  The aim of this paper is to demonstrate the usefulness of the Bayesian Confidence Propagation Neural Network (BCPNN) in the detection of drug-specific and drug-group effects in the database of adverse drug reactions of the World Health Organization Programme for International Drug Monitoring.
  
  
  METHODS
  Examples of drug-adverse reaction combinations highlighted by the BCPNN as quantitative associations were selected. The anatomical therapeutic chemical (ATC) group to which the drug belonged was then identified, and the information component (IC) was calculated for this ATC group and the adverse drug reaction (ADR). The IC of the ATC group with the ADR was then compared with the IC of the drug-ADR by plotting the change in IC and its 95% confidence limit over time for both.
  
  
  RESULTS
  The chosen examples show that the BCPNN data-mining approach can identify drug-specific as well as group effects. In the known examples that served as test cases, beta-blocking agents other than practolol are not associated with sclerosing peritonitis, but all angiotensin-converting enzyme inhibitors are associated with coughing, as are antihistamines with heart-rhythm disorders and antipsychotics with myocarditis. The recently identified association between antipsychotics and myocarditis remains even after consideration of concomitant medication.
  
  
  CONCLUSIONS
  The BCPNN can be used to improve the ability of a signal detection system to highlight group and drug-specific effects.
authors.affiliation: The Uppsala Monitoring Centre, WHO Collaborating Centre for International Drug Monitoring, Sweden.
authors.forename: A
authors.initials: A
authors.lastname: Bate
authors.forename: M
authors.initials: M
authors.lastname: Lindquist
authors.forename: R
authors.initials: R
authors.lastname: Orre
authors.forename: I R
authors.initials: IR
authors.lastname: Edwards
authors.forename: R H B
authors.initials: RH
authors.lastname: Meyboom
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000592:standards* / Q000706:statistics & numerical data*; D001499:Bayes Theorem; D002216:Captopril / Q000009:adverse effects; D003024:Clozapine / Q000009:adverse effects; D016208:Databases, Factual; D016903:Drug Monitoring / Q000379:methods; D064420:Drug-Related Side Effects and Adverse Reactions; D006801:Humans; D016247:Information Storage and Retrieval; D004364:Pharmaceutical Preparations / Q000145:classification; D011217:Practolol / Q000009:adverse effects; D016593:Terfenadine / Q000009:adverse effects; D014944:World Health Organization
chemical_list: D004364:Pharmaceutical Preparations; D016593:Terfenadine; D002216:Captopril; D003024:Clozapine; D011217:Practolol