2021
Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning
Du J, Xiang Y, Sankaranarayanapillai M, Zhang M, Wang J, Si Y, Pham H, Xu H, Chen Y, Tao C. Extracting postmarketing adverse events from safety reports in the vaccine adverse event reporting system (VAERS) using deep learning. Journal Of The American Medical Informatics Association 2021, 28: 1393-1400. PMID: 33647938, PMCID: PMC8279785, DOI: 10.1093/jamia/ocab014.Peer-Reviewed Original ResearchMeSH KeywordsAdverse Drug Reaction Reporting SystemsComputer SystemsDeep LearningGuillain-Barre SyndromeHumansInfluenza VaccinesUnited StatesConceptsDeep learning algorithmsLearning-based methodsVaccine Adverse Event Reporting SystemLearning algorithmArt deep learning algorithmsDeep learning-based methodsConventional machine learning-based methodsMachine learning-based methodsConventional machine learningAdverse Event Reporting SystemGuillain-Barré syndromeLarge modelsAdverse eventsEvent Reporting SystemVAERS reportsDeep learningMachine learningEntity recognitionPeer modelInfluenza vaccine safetyNervous system disordersExact matchVaccine adverse eventsSafety reportsReporting system
2017
Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports
Cai R, Liu M, Hu Y, Melton B, Matheny M, Xu H, Duan L, Waitman L. Identification of adverse drug-drug interactions through causal association rule discovery from spontaneous adverse event reports. Artificial Intelligence In Medicine 2017, 76: 7-15. PMID: 28363289, PMCID: PMC6438384, DOI: 10.1016/j.artmed.2017.01.004.Peer-Reviewed Original ResearchMeSH KeywordsAdverse Drug Reaction Reporting SystemsBayes TheoremData MiningDrug InteractionsDrug-Related Side Effects and Adverse ReactionsHumansMachine LearningConceptsDrug-drug interactionsTraditional association rule mining methodsAssociation rule mining methodAssociation rule discoveryAssociation rule miningRule mining methodAdverse Event Reporting SystemAdverse drug-drug interactionsAdverse event reportsAdverse eventsData-driven discoveryHigher-order associationsRule miningRule discoveryDrug safety surveillanceMining methodsBayesian networkDrug combinationsChallenging taskCausal associationDrug Administration Adverse Event Reporting SystemDDI identificationAdverse drug reactionsCombination of drugsEvent Reporting System
2014
Identifying plausible adverse drug reactions using knowledge extracted from the literature
Shang N, Xu H, Rindflesch T, Cohen T. Identifying plausible adverse drug reactions using knowledge extracted from the literature. Journal Of Biomedical Informatics 2014, 52: 293-310. PMID: 25046831, PMCID: PMC4261011, DOI: 10.1016/j.jbi.2014.07.011.Peer-Reviewed Original ResearchMeSH KeywordsAdverse Drug Reaction Reporting SystemsAlgorithmsBiomedical ResearchData MiningDrug-Related Side Effects and Adverse ReactionsHumansMEDLINENatural Language ProcessingROC CurveSemanticsConceptsPredication-based Semantic IndexingReflective Random IndexingLBD methodsNatural language processing toolsBiomedical literatureDrug-adverse event associationsLanguage processing toolsSemantic indexingElectronic health recordsRandom IndexingHuman reviewVast repositoryDiscovery methodsVolume of knowledgeProcessing toolsEvaluation setHealth recordsData sourcesEvent associationsIndexingDrug-effect relationshipsRepositoryLarge volumesADR associationsReasoning pathways