Data mining for signals in spontaneous reporting databases: proceed with caution.
about
Combining multiple healthcare databases for postmarketing drug and vaccine safety surveillance: why and how?Exploration of the association rules mining technique for the signal detection of adverse drug events in spontaneous reporting systemsEnhancing adverse drug event detection in electronic health records using molecular structure similarity: application to pancreatitisTorsadogenic risk of antipsychotics: combining adverse event reports with drug utilization data across EuropeNovel data-mining methodologies for adverse drug event discovery and analysisCardiovascular and pulmonary adverse events in patients treated with BCR-ABL inhibitors: Data from the FDA Adverse Event Reporting System.A computerized system for detecting signals due to drug-drug interactions in spontaneous reporting systems.Detecting associations between behavioral addictions and dopamine agonists in the Food & Drug Administration's Adverse Event database.Mining multi-item drug adverse effect associations in spontaneous reporting systemsA signal detection method to detect adverse drug reactions using a parametric time-to-event model in simulated cohort data.Antipsychotics and torsadogenic risk: signals emerging from the US FDA Adverse Event Reporting System database.Statistical Mining of Potential Drug Interaction Adverse Effects in FDA's Spontaneous Reporting System.Systems pharmacology augments drug safety surveillancePortable automatic text classification for adverse drug reaction detection via multi-corpus training.Facilitating adverse drug event detection in pharmacovigilance databases using molecular structure similarity: application to rhabdomyolysis.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)Biclustering of adverse drug events in the FDA's spontaneous reporting system.Detection of drug-drug interactions through data mining studies using clinical sources, scientific literature and social media.Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactionsExposure to antibacterial agents with QT liability in 14 European countries: trends over an 8-year periodActive computerized pharmacovigilance using natural language processing, statistics, and electronic health records: a feasibility study.Performance of pharmacovigilance signal-detection algorithms for the FDA adverse event reporting system.Azithromycin and risk of sudden cardiac death: guilty as charged or falsely accused?Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection.Signal detection of potentially drug-induced acute liver injury in children using a multi-country healthcare database network.Evaluation of Electronic Healthcare Databases for Post-Marketing Drug Safety Surveillance and Pharmacoepidemiology in China.Overall conceptual framework for studying the genetics of autoimmune diseases following vaccination: a regulatory perspective.Can network analysis improve pattern recognition among adverse events following immunization reported to VAERS?A novel algorithm for detection of adverse drug reaction signals using a hospital electronic medical record database.AERS spider: an online interactive tool to mine statistical associations in Adverse Event Reporting System.Cardiovascular, ocular and bone adverse reactions associated with thiazolidinediones: a disproportionality analysis of the US FDA adverse event reporting system database.Agreement Among Different Scales for Causality Assessment in Drug-Induced Liver Injury.Detection of adverse drug reaction signals using an electronic health records database: Comparison of the Laboratory Extreme Abnormality Ratio (CLEAR) algorithm.Electronic healthcare databases for active drug safety surveillance: is there enough leverage?Detecting Pharmacovigilance Signals Combining Electronic Medical Records With Spontaneous Reports: A Case Study of Conventional Disease-Modifying Antirheumatic Drugs for Rheumatoid Arthritis
P2860
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P2860
Data mining for signals in spontaneous reporting databases: proceed with caution.
description
2007 nî lūn-bûn
@nan
2007 թուականի Ապրիլին հրատարակուած գիտական յօդուած
@hyw
2007 թվականի ապրիլին հրատարակված գիտական հոդված
@hy
2007年の論文
@ja
2007年論文
@yue
2007年論文
@zh-hant
2007年論文
@zh-hk
2007年論文
@zh-mo
2007年論文
@zh-tw
2007年论文
@wuu
name
Data mining for signals in spontaneous reporting databases: proceed with caution.
@ast
Data mining for signals in spontaneous reporting databases: proceed with caution.
@en
type
label
Data mining for signals in spontaneous reporting databases: proceed with caution.
@ast
Data mining for signals in spontaneous reporting databases: proceed with caution.
@en
prefLabel
Data mining for signals in spontaneous reporting databases: proceed with caution.
@ast
Data mining for signals in spontaneous reporting databases: proceed with caution.
@en
P356
P1476
Data mining for signals in spontaneous reporting databases: proceed with caution.
@en
P2093
Manfred Hauben
Wendy P Stephenson
P304
P356
10.1002/PDS.1323
P577
2007-04-01T00:00:00Z