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Andrew Bate

Researcher at Pfizer

Publications -  107
Citations -  6198

Andrew Bate is an academic researcher from Pfizer. The author has contributed to research in topics: Pharmacovigilance & Medicine. The author has an hindex of 36, co-authored 89 publications receiving 5221 citations. Previous affiliations of Andrew Bate include Umeå University & Uppsala Monitoring Centre.

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A Bayesian neural network method for adverse drug reaction signal generation

TL;DR: The BCPNN will be an extremely useful adjunct to the expert assessment of very large numbers of spontaneously reported ADRs, and can be used in the detection of significant signals from the data set of the WHO Programme on International Drug Monitoring.
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A comparison of measures of disproportionality for signal detection in spontaneous reporting systems for adverse drug reactions.

TL;DR: The objective of this study is to examine the level of concordance of the various estimates to the measure used by the WHO Collaborating Centre for International ADR monitoring, the information component (IC), when applied to the dataset of the Netherlands Pharmacovigilance Foundation Lareb.
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Quantitative signal detection using spontaneous ADR reporting.

TL;DR: The role of Bayesian shrinkage in screening spontaneous reports, the importance of changes over time in screening the properties of the measures and some suggestions as to where emerging research is likely to lead are given.
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Selective serotonin reuptake inhibitors in pregnant women and neonatal withdrawal syndrome: a database analysis

TL;DR: SSRIs, especially paroxetine, should be cautiously managed in the treatment of pregnant women with a psychiatric disorder, and use of these drugs in pregnant women might cause neonatal withdrawal syndrome.
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Antipsychotic drugs and heart muscle disorder in international pharmacovigilance: data mining study

TL;DR: The study shows the potential of bayesian neural networks in analysing data on drug safety by examining the relation between antipsychotic drugs and myocarditis and cardiomyopathy using bayesian statistics implemented in a neural network architecture.