2020
Learning from local to global: An efficient distributed algorithm for modeling time-to-event data
Duan R, Luo C, Schuemie M, Tong J, Liang C, Chang H, Boland M, Bian J, Xu H, Holmes J, Forrest C, Morton S, Berlin J, Moore J, Mahoney K, Chen Y. Learning from local to global: An efficient distributed algorithm for modeling time-to-event data. Journal Of The American Medical Informatics Association 2020, 27: 1028-1036. PMID: 32626900, PMCID: PMC7647322, DOI: 10.1093/jamia/ocaa044.Peer-Reviewed Original Research
2019
Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm
Duan R, Boland M, Liu Z, Liu Y, Chang H, Xu H, Chu H, Schmid C, Forrest C, Holmes J, Schuemie M, Berlin J, Moore J, Chen Y. Learning from electronic health records across multiple sites: A communication-efficient and privacy-preserving distributed algorithm. Journal Of The American Medical Informatics Association 2019, 27: 376-385. PMID: 31816040, PMCID: PMC7025371, DOI: 10.1093/jamia/ocz199.Peer-Reviewed Original ResearchCost-aware active learning for named entity recognition in clinical text
Wei Q, Chen Y, Salimi M, Denny J, Mei Q, Lasko T, Chen Q, Wu S, Franklin A, Cohen T, Xu H. Cost-aware active learning for named entity recognition in clinical text. Journal Of The American Medical Informatics Association 2019, 26: 1314-1322. PMID: 31294792, PMCID: PMC6798575, DOI: 10.1093/jamia/ocz102.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsBig DataComputer SimulationElectronic Health RecordsHumansInformation Storage and RetrievalModels, EconomicNatural Language ProcessingConceptsAnnotation costUser studyActive learningAL methodsAL algorithmCost-CAUSEReal-world environmentsAnnotation taskAnnotation timeAnnotation accuracyEntity recognitionClinical textAnnotation dataPassive learningInformative examplesCurve scoreMost approachesSimulation areaUsersSyntactic featuresLearningCost measuresAlgorithmCostAnnotation
2018
A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set
Rasmy L, Wu Y, Wang N, Geng X, Zheng W, Wang F, Wu H, Xu H, Zhi D. A study of generalizability of recurrent neural network-based predictive models for heart failure onset risk using a large and heterogeneous EHR data set. Journal Of Biomedical Informatics 2018, 84: 11-16. PMID: 29908902, PMCID: PMC6076336, DOI: 10.1016/j.jbi.2018.06.011.Peer-Reviewed Original ResearchConceptsRecurrent neural networkOnset riskCapability of RNNCerner Health FactsHeterogeneous EHR dataHeart failure patientsData setsElectronic health record dataDeep learning modelsDifferent patient populationsNeural network-based predictive modelDifferent patient groupsHealth record dataEHR data setsPredictive modelingSmall data setsFailure patientsPatient groupPatient populationReduction of AUCNeural networkRNN modelRETAIN modelHealth FactsHospital
2017
An active learning-enabled annotation system for clinical named entity recognition
Chen Y, Lask T, Mei Q, Chen Q, Moon S, Wang J, Nguyen K, Dawodu T, Cohen T, Denny J, Xu H. An active learning-enabled annotation system for clinical named entity recognition. BMC Medical Informatics And Decision Making 2017, 17: 82. PMID: 28699546, PMCID: PMC5506567, DOI: 10.1186/s12911-017-0466-9.Peer-Reviewed Original ResearchMeSH KeywordsComputer SimulationHumansMedical InformaticsNatural Language ProcessingProblem-Based LearningConceptsNovel AL algorithmAL algorithmAnnotation timeUser studyEntity recognitionAnnotation systemNatural language processing modelsLanguage processing modelsAnnotation costMedical domainAnnotation processDifferent usersNER modelProcessing modelAlgorithmAL methodsResultsThe simulation resultsUsersSimulation resultsInformation contentFuture workRecognitionLarge numberSystemReal-life settingAn Empirical Study for Impacts of Measurement Errors on EHR based Association Studies.
Duan R, Cao M, Wu Y, Huang J, Denny J, Xu H, Chen Y. An Empirical Study for Impacts of Measurement Errors on EHR based Association Studies. AMIA Annual Symposium Proceedings 2017, 2016: 1764-1773. PMID: 28269935, PMCID: PMC5333313.Peer-Reviewed Original Research
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 diseasesDataset