Use of screening algorithms and computer systems to efficiently signal higher-than-expected combinations of drugs and events in the US FDA's spontaneous reports database

Drug Safety

Volumn 25, Issue 6, 2002, Pages 381-392

  Szarfman, Ana ^a   Machado, Stella G ^a   O'Neill, Robert T ^a  

^a CENTER FOR DRUG EVALUATION AND RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

DRUG;

ALGORITHM; BAYES THEOREM; COMBINATION CHEMOTHERAPY; COMPUTER PROGRAM; COMPUTER SYSTEM; CONFERENCE PAPER; DATA BASE; DATA MINING SYSTEM; DRUG INDUCED DISEASE; DRUG SPECIFICITY; DRUG SURVEILLANCE PROGRAM; DRUG WITHDRAWAL; EVALUATION; FOLLOW UP; FOOD AND DRUG ADMINISTRATION; FREQUENCY ANALYSIS; GAMMA POISSON SHRINKER; HUMAN; MEDICAL RESEARCH; MULTIITEM GAMMA POISSON SHRINKER; PATIENT MONITORING; PRIORITY JOURNAL; SCORING SYSTEM; SCREENING; SIGNAL PROCESSING; UNITED STATES;

EID: 0036300732     PISSN: 01145916     EISSN: None     Source Type: Journal    
DOI: 10.2165/00002018-200225060-00001     Document Type: Conference Paper

References (20)

1. The spontaneous reporting system in the United States
2. A view from regulatory agencies
3. How the US food and drug administration defines and detects adverse drug events
4. Empirical hayes screening for multi-item associations
5. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system
6. Discussion: Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system
7. Discussion: Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system by William DuMouchel
8. Discussion: Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system by William DuMouchel
9. Bayesian data mining in large frequency tables, with an application to the FDA spontaneous reporting system
10. Discussion: A report on the activities of the adverse events working groups: Focus on improving the detection of rare but serious events
11. The application of bayesian data mining and graphic visualization tools to screen FDA's spontaneous reporting system database
12. Analysis and risk assessment of hematological data from clinical trials: Toxicology of the hematopoietic system
13. Standardised data structures and visualisation tools: A way to accelerate the regulatory review of the integrated summary of safety of new drug applications
14. Data mining for postlicensure vaccine safety and policy implications for using results
15. Some FDA perspectives on data mining for pediatric safety assessment
16. Data mining in the US vaccine adverse event reporting system (VAERS): Early detection of intussusception and other events after rotavirus vaccination