A data mining approach for signal detection and analysis

Drug Safety

Volumn 25, Issue 6, 2002, Pages 393-397

  Bate, A ^a,b   Lindquist, M ^a   Edwards, I R ^a   Orre, R ^c  

^a UPPSALA MONITORING CENTRE   (Sweden)

^b UMEÅ UNIVERSITY   (Sweden)

^c STOCKHOLM UNIVERSITY   (Sweden)

Author keywords

[No Author keywords available]

Indexed keywords

DRUG;

ANALYTIC METHOD; ARTIFICIAL NEURAL NETWORK; BAYES THEOREM; BAYESIAN CONFIDENCE PROPAGATION NEURAL NETWORK; CONFERENCE PAPER; DATA ANALYSIS; DATA BASE; DATA MINING APPROACH; DIAGNOSTIC VALUE; DRUG EFFECT; DRUG INDUCED DISEASE; HUMAN; MATHEMATICAL ANALYSIS; PRIORITY JOURNAL; QUANTITATIVE ANALYSIS; SIGNAL DETECTION; WORLD HEALTH ORGANIZATION;

EID: 0036307462     PISSN: 01145916     EISSN: None     Source Type: Journal    
DOI: 10.2165/00002018-200225060-00002     Document Type: Conference Paper

References (10)

1. Harmonisation in pharmacovigilance
2. The role of the WHO programme on international drug monitoring in coordinating worldwide drug safety efforts
3. Pattern recognition using a recurrent neural network and its application to the WHO database
4. Bayesian neural networks with confidence estimations applied to data mining
5. From association to alert: A revised approach to international signal analysis
6. A Bayesian neural network method for adverse drug reaction signal generation
7. A retrospective evaluation of a data mining approach to aid finding new adverse drug reaction signals in the WHO international database
8. Automated classification of signals as group effects or drug specific on the WHO database