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 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
2014
Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning
Liu M, Cai R, Hu Y, Matheny M, Sun J, Hu J, Xu H. Determining molecular predictors of adverse drug reactions with causality analysis based on structure learning. Journal Of The American Medical Informatics Association 2014, 21: 245-251. PMID: 24334612, PMCID: PMC3932464, DOI: 10.1136/amiajnl-2013-002051.Peer-Reviewed Original Research
2013
Word Sense Disambiguation of clinical abbreviations with hyperdimensional computing.
Moon S, Berster B, Xu H, Cohen T. Word Sense Disambiguation of clinical abbreviations with hyperdimensional computing. AMIA Annual Symposium Proceedings 2013, 2013: 1007-16. PMID: 24551390, PMCID: PMC3900125.Peer-Reviewed Original ResearchMeSH KeywordsAbbreviations as TopicAlgorithmsBayes TheoremNatural Language ProcessingSupport Vector MachineConceptsWord sense disambiguationAverage accuracySense disambiguationWord sense disambiguation algorithmSupport vector machineHyperdimensional ComputingNaïve BayesCommon machineClinical documentsVector machineDisambiguation algorithmClinical abbreviationsMedical informationAccurate extractionAlgorithmDisambiguationMachineSuch approachesClinical notesPresent new approachVector transformationNew approachAmbiguous termsComputingAccuracyMachine learning for predicting the response of breast cancer to neoadjuvant chemotherapy
Mani S, Chen Y, Li X, Arlinghaus L, Chakravarthy A, Abramson V, Bhave S, Levy M, Xu H, Yankeelov T. Machine learning for predicting the response of breast cancer to neoadjuvant chemotherapy. Journal Of The American Medical Informatics Association 2013, 20: 688-695. PMID: 23616206, PMCID: PMC3721158, DOI: 10.1136/amiajnl-2012-001332.Peer-Reviewed Original ResearchConceptsNeoadjuvant chemotherapyFeature selectionCycles of NACPredictive model buildingTime most patientsBreast cancer patientsImportant clinical problemCourse of therapyMachine learningDynamic contrast-enhanced MRIContrast-enhanced MRIQuantitative dynamic contrast-enhanced MRIMost patientsTreatment regimenCancer patientsClinical variablesTherapeutic responseBreast cancerPredictive modeling approachClinical problemData show promiseLogistic regressionPatientsMachineDiffusion-weighted MRI data
2012
Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks
Han B, Chen X, Talebizadeh Z, Xu H. Genetic studies of complex human diseases: Characterizing SNP-disease associations using Bayesian networks. BMC Systems Biology 2012, 6: s14. PMID: 23281790, PMCID: PMC3524021, DOI: 10.1186/1752-0509-6-s3-s14.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsAlzheimer DiseaseArtificial IntelligenceAutistic DisorderBayes TheoremComputational BiologyComputer SimulationDatabases, GeneticEpistasis, GeneticGenome-Wide Association StudyHumansMacular DegenerationMarkov ChainsModels, GeneticMonte Carlo MethodPolymorphism, Single NucleotideConceptsEpistatic interaction detectionBayesian network structure learning methodTwo-layer Bayesian networkBayesian network-based methodBayesian networkInteraction detectionMarkov chain Monte Carlo methodsStructure learning methodReal disease dataNetwork-based methodReal GWAS datasetMonte Carlo methodHigh-order epistatic interactionsMachine learningSearch spaceLearning methodsDisease datasetCarlo methodTarget nodeModel complexityStatistical methodsReal dataNew scoring functionComplex human diseasesDatasetLarge-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs
Liu M, Wu Y, Chen Y, Sun J, Zhao Z, Chen X, Matheny M, Xu H. Large-scale prediction of adverse drug reactions using chemical, biological, and phenotypic properties of drugs. Journal Of The American Medical Informatics Association 2012, 19: e28-e35. PMID: 22718037, PMCID: PMC3392844, DOI: 10.1136/amiajnl-2011-000699.Peer-Reviewed Original ResearchConceptsAdverse drug reactionsPost-marketing phaseDrug reactionsSevere adverse drug reactionsImportant adverse drug reactionsWithdrawal of rofecoxibPotential adverse drug reactionsPost-marketing surveillanceADR predictionPatient morbidityClinical trialsMajor causeLarge-scale studiesDrugsMolecular pathwaysDrug developmentPhenotypic featuresSignificant improvementPhenotypic characteristicsEarly stages