Disproportionality analysis for signal detection of implantable cardioverter-defibrillator-related adverse events in the Food and Drug Administration Medical Device Reporting System
Adverse events; Data mining; Disproportionality analysis; Leads; MAUDE; Safety
Indexed keywords
ADVERSE OUTCOME;
ARTICLE;
DATA MINING;
DEFIBRILLATOR;
DEVICE SAFETY;
ELECTRIC SHOCK;
ELECTRODE;
FEASIBILITY STUDY;
HUMAN;
IMPEDANCE;
PRIORITY JOURNAL;
RETROSPECTIVE STUDY;
SAFETY SIGNAL;
SIGNAL DETECTION;
BAYES THEOREM;
DATABASES, FACTUAL;
DEFIBRILLATORS, IMPLANTABLE;
ELECTRIC INJURIES;
EQUIPMENT FAILURE;
EQUIPMENT SAFETY;
HUMANS;
PRODUCT SURVEILLANCE, POSTMARKETING;
RETROSPECTIVE STUDIES;
SAFETY-BASED MEDICAL DEVICE WITHDRAWALS;
TIME FACTORS;
UNITED STATES;
UNITED STATES FOOD AND DRUG ADMINISTRATION;
Bayesian data mining in large frequency tables, with an application to the FDA Spontaneous Reporting System
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Manufacturer and User Facility Device Experience Database - (MAUDE). [13 August 2010].
(2010)
5
84855352729
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Empirical Bayes Screening for Multi-Item Associations. Proceedings of the ACMSIGKDD 2001 International Conference on Knowledge Discovery from Data
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Association of asthma therapy and Churg-Strauss syndrome: an analysis of postmarketing surveillance data
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Antimicrobials and the risk of torsades de pointes: the contribution from data mining of the US FDA Adverse Event Reporting System
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