2023
Representing and utilizing clinical textual data for real world studies: An OHDSI approach
Keloth V, Banda J, Gurley M, Heider P, Kennedy G, Liu H, Liu F, Miller T, Natarajan K, V Patterson O, Peng Y, Raja K, Reeves R, Rouhizadeh M, Shi J, Wang X, Wang Y, Wei W, Williams A, Zhang R, Belenkaya R, Reich C, Blacketer C, Ryan P, Hripcsak G, Elhadad N, Xu H. Representing and utilizing clinical textual data for real world studies: An OHDSI approach. Journal Of Biomedical Informatics 2023, 142: 104343. PMID: 36935011, PMCID: PMC10428170, DOI: 10.1016/j.jbi.2023.104343.Peer-Reviewed Original ResearchMeSH KeywordsData ScienceElectronic Health RecordsHumansMedical InformaticsNarrationNatural Language ProcessingConceptsNatural language processingCommon data modelTextual dataNLP solutionObservational Health Data SciencesOMOP Common Data ModelSpecific use casesObservational Medical Outcomes Partnership Common Data ModelHealth Data SciencesRepresentation of informationUse casesElectronic health recordsReal-world evidence generationData scienceClinical textData modelClinical notesLanguage processingHealth recordsLoad dataClinical documentationCurrent applicationsInformationWorkflowEvidence generation
2022
Novel informatics approaches to COVID-19 Research: From methods to applications
Xu H, Buckeridge D, Wang F, Tarczy-Hornoch P. Novel informatics approaches to COVID-19 Research: From methods to applications. Journal Of Biomedical Informatics 2022, 129: 104028. PMID: 35181495, PMCID: PMC8847074, DOI: 10.1016/j.jbi.2022.104028.Peer-Reviewed Original Research
2019
Special issue of BMC medical informatics and decision making on health natural language processing
Vydiswaran V, Zhang Y, Wang Y, Xu H. Special issue of BMC medical informatics and decision making on health natural language processing. BMC Medical Informatics And Decision Making 2019, 19: 76. PMID: 30943961, PMCID: PMC6448180, DOI: 10.1186/s12911-019-0777-0.Peer-Reviewed Original Research
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
Entity recognition from clinical texts via recurrent neural network
Liu Z, Yang M, Wang X, Chen Q, Tang B, Wang Z, Xu H. Entity recognition from clinical texts via recurrent neural network. BMC Medical Informatics And Decision Making 2017, 17: 67. PMID: 28699566, PMCID: PMC5506598, DOI: 10.1186/s12911-017-0468-7.Peer-Reviewed Original ResearchMeSH KeywordsElectronic Health RecordsHumansMachine LearningMedical InformaticsNatural Language ProcessingNeural Networks, ComputerConceptsRecurrent neural networkNatural language processingEntity recognitionClinical textTraditional machineNeural networkClinical natural language processingMedical concept extractionHand-crafted featuresClinical entity recognitionDeep learning methodsClinical event detectionConditional Random FieldsSupport vector machineI2b2 NLP challengePerformance of LSTMTypes of entitiesClinical domainsContext informationFeature engineeringConcept extractionDe-identificationEvent detectionKnowledge basesLSTM layersAn 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 settingIntroduction: the International Conference on Intelligent Biology and Medicine (ICIBM) 2016: special focus on medical informatics and big data
Tao C, Gong Y, Xu H, Zhao Z. Introduction: the International Conference on Intelligent Biology and Medicine (ICIBM) 2016: special focus on medical informatics and big data. BMC Medical Informatics And Decision Making 2017, 17: 77. PMID: 28699553, PMCID: PMC5506607, DOI: 10.1186/s12911-017-0462-0.Peer-Reviewed Original ResearchConceptsIntelligent BiologyBig dataMedical informaticsClinical natural language processingNatural language processingSocial media applicationsData miningElectronic health recordsMedical domainData scienceLanguage processingMedia applicationsHealth recordsInformaticsSpecial themeSafety analysisMiningPersonalizationIssuesPatient safety analysesInternational ConferenceResearch articlesProcessingHealthcareRecent advances
2015
Parsing clinical text: how good are the state-of-the-art parsers?
Jiang M, Huang Y, Fan J, Tang B, Denny J, Xu H. Parsing clinical text: how good are the state-of-the-art parsers? BMC Medical Informatics And Decision Making 2015, 15: s2. PMID: 26045009, PMCID: PMC4460747, DOI: 10.1186/1472-6947-15-s1-s2.Peer-Reviewed Original Research
2007
Using contextual and lexical features to restructure and validate the classification of biomedical concepts
Fan J, Xu H, Friedman C. Using contextual and lexical features to restructure and validate the classification of biomedical concepts. BMC Bioinformatics 2007, 8: 264. PMID: 17650333, PMCID: PMC2014782, DOI: 10.1186/1471-2105-8-264.Peer-Reviewed Original ResearchMeSH KeywordsBiomedical ResearchMedical InformaticsSemanticsSoftwareTerminology as TopicUnified Medical Language SystemConceptsUnified Medical Language SystemString-based approachesMean reciprocal rankReciprocal rankNatural language processingError rateContextual featuresLexical featuresIntegration of dataLow error rateReasoning systemAutomatic approachComplementary classifiersLanguage processingClassification approachBiomedical terminologiesClassification errorOntological conceptsBiomedical conceptsOntological termsSyntactic approachLanguage systemClassifierSyntactic featuresOntology