2024
SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials
Lee K, Paek H, Huang L, Hilton C, Datta S, Higashi J, Ofoegbu N, Wang J, Rubinstein S, Cowan A, Kwok M, Warner J, Xu H, Wang X. SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials. Informatics In Medicine Unlocked 2024, 50: 101589. PMID: 39493413, PMCID: PMC11530223, DOI: 10.1016/j.imu.2024.101589.Peer-Reviewed Original ResearchAntibody-drug conjugatesOverall response rateMultiple myelomaF1 scoreCAR-TComplete responseBispecific antibodiesComparative performance analysisClinical trial studyClinical trial outcomesLanguage modelAccurate data extractionTherapy subgroupFine granularityOncology clinical trialsAdverse eventsClinical decision-makingPerformance analysisClinical trialsInnovative therapiesDiverse therapiesClinical trial abstractsCancer domainData elementsTherapy
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 ResearchConceptsDeep 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
A comparative study of different methods for automatic identification of clopidogrel-induced bleedings in electronic health records.
Lee H, Jiang M, Wu Y, Shaffer C, Cleator J, Friedman E, Lewis J, Roden D, Denny J, Xu H. A comparative study of different methods for automatic identification of clopidogrel-induced bleedings in electronic health records. AMIA Joint Summits On Translational Science Proceedings 2017, 2017: 185-192. PMID: 28815128, PMCID: PMC5543340.Peer-Reviewed Original ResearchElectronic health recordsAdverse drug reactionsMachine learning-based methodsLearning-based methodsHealth recordsRule-based methodReasonable recallAutomatic identificationValuable data sourceAutomatic methodTemporality informationCertain adverse drug reactionsData sourcesIdentification of patientsPharmacogenomic studiesManual chart reviewInformatics approachAdverse eventsChart reviewDrug reactionsHigh precisionFunction-based methodScoring methodDifferent typesBleedingIdentification 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 ResearchConceptsDrug-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
2012
Optimizing Drug Outcomes Through Pharmacogenetics: A Case for Preemptive Genotyping
Schildcrout J, Denny J, Bowton E, Gregg W, Pulley J, Basford M, Cowan J, Xu H, Ramirez A, Crawford D, Ritchie M, Peterson J, Masys D, Wilke R, Roden D. Optimizing Drug Outcomes Through Pharmacogenetics: A Case for Preemptive Genotyping. Clinical Pharmacology & Therapeutics 2012, 92: 235-242. PMID: 22739144, PMCID: PMC3785311, DOI: 10.1038/clpt.2012.66.Peer-Reviewed Original ResearchConceptsVanderbilt University Medical CenterAdverse eventsPreemptive genotypingPotential adverse eventsUniversity Medical CenterHome patientsPharmacogenetic associationsMedical CenterVariant allelesMedicationsDrug outcomesPatient safetyDrug decision makingRelevant genetic variantsRoutine integrationTarget drugsGenetic variantsOutcomesFrequency of opportunitiesGenotypingSafetyPrescribingPatientsCohortPharmacogenetics