Literature DB >> 17019675

Data mining for signals in spontaneous reporting databases: proceed with caution.

Wendy P Stephenson1, Manfred Hauben.   

Abstract

PURPOSE: To provide commentary and points of caution to consider before incorporating data mining as a routine component of any Pharmacovigilance program, and to stimulate further research aimed at better defining the predictive value of these new tools as well as their incremental value as an adjunct to traditional methods of post-marketing surveillance. METHODS/
RESULTS: Commentary includes review of current data mining methodologies employed and their limitations, caveats to consider in the use of spontaneous reporting databases and caution against over-confidence in the results of data mining.
CONCLUSIONS: Future research should focus on more clearly delineating the limitations of the various quantitative approaches as well as the incremental value that they bring to traditional methods of pharmacovigilance.

Mesh:

Year:  2007        PMID: 17019675     DOI: 10.1002/pds.1323

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


  41 in total

1.  A signal detection method to detect adverse drug reactions using a parametric time-to-event model in simulated cohort data.

Authors:  Victoria R Cornelius; Odile Sauzet; Stephen J W Evans
Journal:  Drug Saf       Date:  2012-07-01       Impact factor: 5.606

2.  Biclustering of adverse drug events in the FDA's spontaneous reporting system.

Authors:  R Harpaz; H Perez; H S Chase; R Rabadan; G Hripcsak; C Friedman
Journal:  Clin Pharmacol Ther       Date:  2010-12-29       Impact factor: 6.875

3.  Temporal data mining for adverse events following immunization in nationwide Danish healthcare databases.

Authors:  Henrik Svanström; Torbjörn Callréus; Anders Hviid
Journal:  Drug Saf       Date:  2010-11-01       Impact factor: 5.606

4.  Antimicrobials and the risk of torsades de pointes: the contribution from data mining of the US FDA Adverse Event Reporting System.

Authors:  Elisabetta Poluzzi; Emanuel Raschi; Domenico Motola; Ugo Moretti; Fabrizio De Ponti
Journal:  Drug Saf       Date:  2010-04-01       Impact factor: 5.606

5.  Active computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study.

Authors:  Xiaoyan Wang; George Hripcsak; Marianthi Markatou; Carol Friedman
Journal:  J Am Med Inform Assoc       Date:  2009-03-04       Impact factor: 4.497

6.  Prospective data mining of six products in the US FDA Adverse Event Reporting System: disposition of events identified and impact on product safety profiles.

Authors:  Steven Bailey; Ajay Singh; Robert Azadian; Peter Huber; Michael Blum
Journal:  Drug Saf       Date:  2010-02-01       Impact factor: 5.606

7.  A computerized system for detecting signals due to drug-drug interactions in spontaneous reporting systems.

Authors:  Yifeng Qian; Xiaofei Ye; Wenmin Du; Jingtian Ren; Yalin Sun; Hainan Wang; Baozhang Luo; Qingbin Gao; Meijing Wu; Jia He
Journal:  Br J Clin Pharmacol       Date:  2010-01       Impact factor: 4.335

8.  Agreement Among Different Scales for Causality Assessment in Drug-Induced Liver Injury.

Authors:  Saibal Das; Sapan K Behera; Alphienes S Xavier; Srinivas Velupula; Steven A Dkhar; Sandhiya Selvarajan
Journal:  Clin Drug Investig       Date:  2018-03       Impact factor: 2.859

9.  Automatic signal extraction, prioritizing and filtering approaches in detecting post-marketing cardiovascular events associated with targeted cancer drugs from the FDA Adverse Event Reporting System (FAERS).

Authors:  Rong Xu; Quanqiu Wang
Journal:  J Biomed Inform       Date:  2013-10-28       Impact factor: 6.317

Review 10.  Exposure to antibacterial agents with QT liability in 14 European countries: trends over an 8-year period.

Authors:  Emanuel Raschi; Elisabetta Poluzzi; Chiara Zuliani; Arno Muller; Herman Goossens; Fabrizio De Ponti
Journal:  Br J Clin Pharmacol       Date:  2008-11-17       Impact factor: 4.335

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