2020
A study of entity-linking methods for normalizing Chinese diagnosis and procedure terms to ICD codes
Wang Q, Ji Z, Wang J, Wu S, Lin W, Li W, Ke L, Xiao G, Jiang Q, Xu H, Zhou Y. A study of entity-linking methods for normalizing Chinese diagnosis and procedure terms to ICD codes. Journal Of Biomedical Informatics 2020, 105: 103418. PMID: 32298846, DOI: 10.1016/j.jbi.2020.103418.Peer-Reviewed Original ResearchMeSH KeywordsChinaClinical CodingInternational Classification of DiseasesNeural Networks, ComputerSupport Vector MachineConceptsBM25 algorithmConcept rankingConcept generationConvolutional neural network approachNeural network approachRanking-based methodRanking methodSupport vector machineProcedure termsBetter performanceVector machineDifferent algorithmsMedical codingNetwork approachAlgorithmICD codesBERTExtended versionGood accuracyKnowledgebaseDisease termsClinical termsMatch criteriaCodeChinese diagnosis
2015
Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations
Chen Y, Sun J, Huang L, Xu H, Zhao Z. Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations. BioMed Research International 2015, 2015: 491502. PMID: 26539502, PMCID: PMC4619847, DOI: 10.1155/2015/491502.Peer-Reviewed Original ResearchMeSH KeywordsFemaleHumansIntestine, LargeLiverLungMaleMutationNeoplasms, Unknown PrimaryPancreasSkinSupport Vector MachineConceptsMachine learningF-measureAvailable big dataSupport vector machineBig dataVector machineClassification experimentsAccurate classificationCancer classificationGene function informationMachineSomatic mutation informationClassificationMutation informationFunction informationLearningGene symbolsInformationGene featuresGreat opportunityPerformanceSomatic mutation dataMutation dataAccuracyPrediction
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
A comprehensive study of named entity recognition in Chinese clinical text
Lei J, Tang B, Lu X, Gao K, Jiang M, Xu H. A comprehensive study of named entity recognition in Chinese clinical text. Journal Of The American Medical Informatics Association 2013, 21: 808-814. PMID: 24347408, PMCID: PMC4147609, DOI: 10.1136/amiajnl-2013-002381.Peer-Reviewed Original ResearchApplying active learning to high-throughput phenotyping algorithms for electronic health records data
Chen Y, Carroll R, Hinz E, Shah A, Eyler A, Denny J, Xu H. Applying active learning to high-throughput phenotyping algorithms for electronic health records data. Journal Of The American Medical Informatics Association 2013, 20: e253-e259. PMID: 23851443, PMCID: PMC3861916, DOI: 10.1136/amiajnl-2013-001945.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceElectronic Health RecordsGenetic Association StudiesHumansPhenotypeSupport Vector MachineConceptsActive learningUnrefined featuresSupervised Machine Learning AlgorithmsRefined featuresPhenotyping algorithmElectronic health record dataMachine Learning AlgorithmsHealth record dataVenous thromboembolismRheumatoid arthritisFeature engineeringDomain expertsDomain knowledgePhenotyping tasksLearning algorithmFeature setsLearning approachColorectal cancerAL approachCurve scorePassive learning approachHigh-throughput phenotyping methodsAlgorithmSmall setRecord dataWord 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 termsComputingAccuracyApplying active learning to supervised word sense disambiguation in MEDLINE
Chen Y, Cao H, Mei Q, Zheng K, Xu H. Applying active learning to supervised word sense disambiguation in MEDLINE. Journal Of The American Medical Informatics Association 2013, 20: 1001-1006. PMID: 23364851, PMCID: PMC3756255, DOI: 10.1136/amiajnl-2012-001244.Peer-Reviewed Original Research
2012
Large-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 stagesRecognition of medication information from discharge summaries using ensembles of classifiers
Doan S, Collier N, Xu H, Duy P, Phuong T. Recognition of medication information from discharge summaries using ensembles of classifiers. BMC Medical Informatics And Decision Making 2012, 12: 36. PMID: 22564405, PMCID: PMC3502425, DOI: 10.1186/1472-6947-12-36.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceDecision Support TechniquesFemaleHumansInformation Storage and RetrievalInstitutional Management TeamsMaleMedication SystemsNatural Language ProcessingPatient DischargePattern Recognition, AutomatedPharmaceutical PreparationsReproducibility of ResultsSemanticsSoftware DesignSupport Vector MachineConceptsConditional Random FieldsNatural language processingClinical natural language processingSupport vector machineBest F-scoreEnsemble classifierF-scoreClinical textIndividual classifiersVoting methodMajority votingLocal support vector machineSupervised machine learning methodsClinical entity recognitionClinical NLP systemsDifferent voting strategiesEntity recognition systemRule-based systemEnsemble of classifiersMachine learning methodsRule-based methodI2b2 NLP challengeEntity recognitionRecognition systemNLP systems
2011
Detecting abbreviations in discharge summaries using machine learning methods.
Wu Y, Rosenbloom S, Denny J, Miller R, Mani S, Giuse D, Xu H. Detecting abbreviations in discharge summaries using machine learning methods. AMIA Annual Symposium Proceedings 2011, 2011: 1541-9. PMID: 22195219, PMCID: PMC3243185.Peer-Reviewed Original ResearchConceptsNatural language processingMachine learning methodsHighest F-measureF-measureClinical natural language processingLexical resourcesClinical abbreviationsTraining setPre-defined featuresRandom forest classifierDomain expertsML algorithmsML classifiersLanguage processingVoting schemeLearning methodsDischarge summariesForest classifierTest setClassifierCorpus-based methodSetResourcesAlgorithmAbbreviations