M
Marie Lindquist
Researcher at Uppsala Monitoring Centre
Publications - 61
Citations - 4491
Marie Lindquist is an academic researcher from Uppsala Monitoring Centre. The author has contributed to research in topics: Pharmacovigilance & Bcpnn. The author has an hindex of 28, co-authored 59 publications receiving 3917 citations. Previous affiliations of Marie Lindquist include National Board of Health and Welfare.
Papers
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Journal ArticleDOI
A Bayesian neural network method for adverse drug reaction signal generation
Andrew Bate,Marie Lindquist,I. R. Edwards,Sten Olsson,Roland Orre,Anders Lansner,R.M. De Freitas +6 more
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.
Eugène van Puijenbroek,Andrew Bate,Andrew Bate,Hubert G. M. Leufkens,Marie Lindquist,Roland Orre,Antoine C. G. Egberts +6 more
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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VigiBase, the WHO Global ICSR Database System: Basic Facts
TL;DR: The main aim of the WHO International Drug Monitoring Programme, started in 1968, is to identify the earliest possible pharmacovigilance signals, and the VigiBase system includes a web-based reporting tool, an automated signal detection process using advanced data mining, and search facilities.
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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.
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A Retrospective Evaluation of a Data Mining Approach to Aid Finding New Adverse Drug Reaction Signals in the WHO International Database
TL;DR: A new signalling process using Bayesian logic, applied to data mining, within a confidence propagation neural network (Bayesian Confidence Propagation Neural Network; BCPNN) is developed to aid the clinical review of new adverse drug reactions.