title: JADE: a tool for medical researchers to explore adverse drug events using health claims data.
abstract: OBJECTIVE
  The objective of our project was to create a tool for physicians to explore health claims data with regard to adverse drug reactions. The Java Adverse Drug Event (JADE) tool should enable the analysis of prescribed drugs in connection with diagnoses from hospital stays.
  
  
  METHODS
  We calculated the number of days drugs were taken by using the defined daily doses and estimated possible interactions between dispensed drugs using the Austria Codex, a database including drug-drug interactions. The JADE tool was implemented using Java, R and a PostgreSQL database.
  
  
  RESULTS
  Beside an overview of the study cohort which includes selection of gender and age groups, selected statistical methods like association rule learning, logistic regression model and the number needed to harm have been implemented.
  
  
  CONCLUSIONS
  The JADE tool can support physicians during their planning of clinical trials by showing the occurrences of adverse drug events with population based information.
authors.affiliation: Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna , Austria.
authors.forename: D
authors.initials: D
authors.lastname: Edlinger
authors.affiliation: Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna , Austria.
authors.forename: S K
authors.initials: SK
authors.lastname: Sauter
authors.affiliation: Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna , Austria.
authors.forename: C
authors.initials: C
authors.lastname: Rinner
authors.affiliation: Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna , Austria.
authors.forename: L M
authors.initials: LM
authors.lastname: Neuhofer
authors.affiliation: Department of Clinical Pharmacology, Medical University of Vienna , Austria.
authors.forename: M
authors.initials: M
authors.lastname: Wolzt
authors.affiliation: Research Group Scientific Computing, University of Vienna , Austria.
authors.forename: W
authors.initials: W
authors.lastname: Grossmann
authors.affiliation: Main Association of Austrian Social Security Organizations , Vienna, Austria.
authors.forename: G
authors.initials: G
authors.lastname: Endel
authors.affiliation: Center for Medical Statistics, Informatics and Intelligent Systems, Medical University of Vienna , Austria.
authors.forename: W
authors.initials: W
authors.lastname: Gall
mesh_terms: D016907:Adverse Drug Reaction Reporting Systems / Q000706:statistics & numerical data*; D001317:Austria / Q000453:epidemiology; D035843:Biomedical Research / Q000379:methods; D057225:Data Mining / Q000379:methods*; D064420:Drug-Related Side Effects and Adverse Reactions / Q000453:epidemiology*; D057286:Electronic Health Records / Q000706:statistics & numerical data*; D055695:Electronic Prescribing / Q000706:statistics & numerical data*; D006801:Humans; D007342:Insurance Benefits / Q000706:statistics & numerical data*; D008498:Medical Record Linkage / Q000379:methods; D015995:Prevalence; D012984:Software
keywords: Patient safety; adverse drug events; adverse drug reaction reporting systems; drug-drug interactions; medical informatics applications