Literature DB >> 23208789

The EU-ADR Web Platform: delivering advanced pharmacovigilance tools.

José Luis Oliveira1, Pedro Lopes, Tiago Nunes, David Campos, Scott Boyer, Ernst Ahlberg, Erik M van Mulligen, Jan A Kors, Bharat Singh, Laura I Furlong, Ferran Sanz, Anna Bauer-Mehren, Maria C Carrascosa, Jordi Mestres, Paul Avillach, Gayo Diallo, Carlos Díaz Acedo, Johan van der Lei.   

Abstract

PURPOSE: Pharmacovigilance methods have advanced greatly during the last decades, making post-market drug assessment an essential drug evaluation component. These methods mainly rely on the use of spontaneous reporting systems and health information databases to collect expertise from huge amounts of real-world reports. The EU-ADR Web Platform was built to further facilitate accessing, monitoring and exploring these data, enabling an in-depth analysis of adverse drug reactions risks.
METHODS: The EU-ADR Web Platform exploits the wealth of data collected within a large-scale European initiative, the EU-ADR project. Millions of electronic health records, provided by national health agencies, are mined for specific drug events, which are correlated with literature, protein and pathway data, resulting in a rich drug-event dataset. Next, advanced distributed computing methods are tailored to coordinate the execution of data-mining and statistical analysis tasks. This permits obtaining a ranked drug-event list, removing spurious entries and highlighting relationships with high risk potential.
RESULTS: The EU-ADR Web Platform is an open workspace for the integrated analysis of pharmacovigilance datasets. Using this software, researchers can access a variety of tools provided by distinct partners in a single centralized environment. Besides performing standalone drug-event assessments, they can also control the pipeline for an improved batch analysis of custom datasets. Drug-event pairs can be substantiated and statistically analysed within the platform's innovative working environment.
CONCLUSIONS: A pioneering workspace that helps in explaining the biological path of adverse drug reactions was developed within the EU-ADR project consortium. This tool, targeted at the pharmacovigilance community, is available online at https://bioinformatics.ua.pt/euadr/.
Copyright © 2012 John Wiley & Sons, Ltd.

Mesh:

Year:  2012        PMID: 23208789     DOI: 10.1002/pds.3375

Source DB:  PubMed          Journal:  Pharmacoepidemiol Drug Saf        ISSN: 1053-8569            Impact factor:   2.890


  22 in total

1.  A Multiagent System for Integrated Detection of Pharmacovigilance Signals.

Authors:  Vassilis Koutkias; Marie-Christine Jaulent
Journal:  J Med Syst       Date:  2015-11-21       Impact factor: 4.460

2.  Social Media Listening for Routine Post-Marketing Safety Surveillance.

Authors:  Gregory E Powell; Harry A Seifert; Tjark Reblin; Phil J Burstein; James Blowers; J Alan Menius; Jeffery L Painter; Michele Thomas; Carrie E Pierce; Harold W Rodriguez; John S Brownstein; Clark C Freifeld; Heidi G Bell; Nabarun Dasgupta
Journal:  Drug Saf       Date:  2016-05       Impact factor: 5.606

3.  Transparent Reporting on Research Using Unstructured Electronic Health Record Data to Generate 'Real World' Evidence of Comparative Effectiveness and Safety.

Authors:  Shirley V Wang; Olga V Patterson; Joshua J Gagne; Jeffrey S Brown; Robert Ball; Pall Jonsson; Adam Wright; Li Zhou; Wim Goettsch; Andrew Bate
Journal:  Drug Saf       Date:  2019-11       Impact factor: 5.606

4.  Identifying plausible adverse drug reactions using knowledge extracted from the literature.

Authors:  Ning Shang; Hua Xu; Thomas C Rindflesch; Trevor Cohen
Journal:  J Biomed Inform       Date:  2014-07-19       Impact factor: 6.317

Review 5.  "Big data" and the electronic health record.

Authors:  M K Ross; W Wei; L Ohno-Machado
Journal:  Yearb Med Inform       Date:  2014-08-15

6.  Markov Logic Networks for Adverse Drug Event Extraction from Text.

Authors:  Sriraam Natarajan; Vishal Bangera; Tushar Khot; Jose Picado; Anurag Wazalwar; Vitor Santos Costa; David Page; Michael Caldwell
Journal:  Knowl Inf Syst       Date:  2016-08-08       Impact factor: 2.822

7.  Conducting Privacy-Preserving Multivariable Propensity Score Analysis When Patient Covariate Information Is Stored in Separate Locations.

Authors:  Justin Bohn; Wesley Eddings; Sebastian Schneeweiss
Journal:  Am J Epidemiol       Date:  2017-03-15       Impact factor: 4.897

8.  Reporting of clinical trial safety results in ClinicalTrials.gov for FDA-approved drugs: A cross-sectional analysis.

Authors:  Krista Y Chen; Erin M Borglund; Emma Charlotte Postema; Adam G Dunn; Florence T Bourgeois
Journal:  Clin Trials       Date:  2022-04-28       Impact factor: 2.599

9.  Targets of drugs are generally, and targets of drugs having side effects are specifically good spreaders of human interactome perturbations.

Authors:  Áron R Perez-Lopez; Kristóf Z Szalay; Dénes Türei; Dezső Módos; Katalin Lenti; Tamás Korcsmáros; Peter Csermely
Journal:  Sci Rep       Date:  2015-05-11       Impact factor: 4.379

10.  Drug-induced acute myocardial infarction: identifying 'prime suspects' from electronic healthcare records-based surveillance system.

Authors:  Preciosa M Coloma; Martijn J Schuemie; Gianluca Trifirò; Laura Furlong; Erik van Mulligen; Anna Bauer-Mehren; Paul Avillach; Jan Kors; Ferran Sanz; Jordi Mestres; José Luis Oliveira; Scott Boyer; Ernst Ahlberg Helgee; Mariam Molokhia; Justin Matthews; David Prieto-Merino; Rosa Gini; Ron Herings; Giampiero Mazzaglia; Gino Picelli; Lorenza Scotti; Lars Pedersen; Johan van der Lei; Miriam Sturkenboom
Journal:  PLoS One       Date:  2013-08-28       Impact factor: 3.240

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