2024
Introduction to Natural Language Processing of Clinical Text
Demner Fushman D, Xu H. Introduction to Natural Language Processing of Clinical Text. Cognitive Informatics In Biomedicine And Healthcare 2024, 3-11. DOI: 10.1007/978-3-031-55865-8_1.Peer-Reviewed Original ResearchNatural language processingLanguage processingComplex language processingBiomedical natural language processingClinical natural language processingLanguage generation tasksClinical language processingBiomedical language processingLanguage modelClinical textGeneration taskMachine learningDelivery of informationClinical languageLanguageMedical Concept Normalization
Xu H, Demner Fushman D, Hong N, Raja K. Medical Concept Normalization. Cognitive Informatics In Biomedicine And Healthcare 2024, 137-164. DOI: 10.1007/978-3-031-55865-8_6.Peer-Reviewed Original ResearchConcept normalizationDeep learning-based techniquesMedical concept normalizationLearning-based techniquesContemporary machine learningRule-based methodologyAnnotated corpusNLP systemsMachine learningComputing applicationsBiomedical terminologiesNormalization approachStandardized terminologyOntologyTaskLearningA scoping review of fair machine learning techniques when using real-world data
Huang Y, Guo J, Chen W, Lin H, Tang H, Wang F, Xu H, Bian J. A scoping review of fair machine learning techniques when using real-world data. Journal Of Biomedical Informatics 2024, 151: 104622. PMID: 38452862, PMCID: PMC11146346, DOI: 10.1016/j.jbi.2024.104622.Peer-Reviewed Original ResearchConceptsReal-world dataHealth care applicationsHealth care domainMachine learningArtificial intelligenceCare applicationsMulti-modal dataIntegration of artificial intelligenceMachine learning techniquesPre-processing techniquesCare domainBias mitigation approachesPublic datasetsAI/ML modelsModel fairnessLearning techniquesOptimal fairnessHealth care dataAI toolsHealth careAlgorithmic biasML modelsAI/MLFairnessBias issuesArtificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets
Li Y, Tao W, Li Z, Sun Z, Li F, Fenton S, Xu H, Tao C. Artificial intelligence-powered pharmacovigilance: A review of machine and deep learning in clinical text-based adverse drug event detection for benchmark datasets. Journal Of Biomedical Informatics 2024, 152: 104621. PMID: 38447600, DOI: 10.1016/j.jbi.2024.104621.Peer-Reviewed Original ResearchNamed-entity recognitionEnd-to-end tasksEnd-to-endMachine learningBenchmark datasetsAdverse drug event extractionNamed-entity recognition taskLearning modelsAdverse drug event detectionBidirectional Encoder RepresentationsDeep learning techniquesDeep learning methodsDeep learning modelsEffectiveness of machine learningDeep learning methodologyMachine learning modelsSocial media dataEncoder RepresentationsEvent detectionDeep learningLearning techniquesMultilayer perceptronLearning methodsMedia dataRC task
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
2020
Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies
Rasmy L, Tiryaki F, Zhou Y, Xiang Y, Tao C, Xu H, Zhi D. Representation of EHR data for predictive modeling: a comparison between UMLS and other terminologies. Journal Of The American Medical Informatics Association 2020, 27: 1593-1599. PMID: 32930711, PMCID: PMC7647355, DOI: 10.1093/jamia/ocaa180.Peer-Reviewed Original ResearchConceptsUnified Medical Language SystemRecurrent neural networkNeural networkPrediction performanceLogistic regressionPredictive modelingDeep learningData aggregationElectronic health record dataMachine learningRisk predictionBetter prediction performanceDengue hemorrhagic feverHealth record dataEHR dataCancer predictionLarge vocabularyDifferent tasksPredictive modelHeart failureDiabetes patientsPancreatic cancerClinical dataHemorrhagic feverICD-9
2015
Recognizing Disjoint Clinical Concepts in Clinical Text Using Machine Learning-based Methods.
Tang B, Chen Q, Wang X, Wu Y, Zhang Y, Jiang M, Wang J, Xu H. Recognizing Disjoint Clinical Concepts in Clinical Text Using Machine Learning-based Methods. AMIA Annual Symposium Proceedings 2015, 2015: 1184-93. PMID: 26958258, PMCID: PMC4765674.Peer-Reviewed Original ResearchClassification 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 ResearchConceptsMachine learningF-measureAvailable big dataSupport vector machineBig dataVector machineClassification experimentsAccurate classificationCancer classificationGene function informationMachineSomatic mutation informationClassificationMutation informationFunction informationLearningGene symbolsInformationGene featuresGreat opportunityPerformanceSomatic mutation dataMutation dataAccuracyPredictionA study of active learning methods for named entity recognition in clinical text
Chen Y, Lasko T, Mei Q, Denny J, Xu H. A study of active learning methods for named entity recognition in clinical text. Journal Of Biomedical Informatics 2015, 58: 11-18. PMID: 26385377, PMCID: PMC4934373, DOI: 10.1016/j.jbi.2015.09.010.Peer-Reviewed Original ResearchConceptsClinical NER tasksMachine learningAnnotation costF-measureEntity recognitionNER taskActive learningLearning methodsI2b2/VA NLP challengeNatural language processing systemsPerformance of MLClinical natural language processing (NLP) systemsSequential labeling tasksSupervised machine learningAL methodsLanguage processing systemDiversity-based methodReal-time settingActive learning methodsNew AL methodsNER corpusDomain expertsUncertainty samplingAnnotation effortClinical text
2013
Machine 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 dataRecognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features
Tang B, Cao H, Wu Y, Jiang M, Xu H. Recognizing clinical entities in hospital discharge summaries using Structural Support Vector Machines with word representation features. BMC Medical Informatics And Decision Making 2013, 13: s1. PMID: 23566040, PMCID: PMC3618243, DOI: 10.1186/1472-6947-13-s1-s1.Peer-Reviewed Original ResearchConceptsStructural support vector machineWord representation featuresClinical NER tasksConditional Random FieldsSupport vector machinePerformance of MLClinical NER systemMachine learningRepresentation featuresNER systemNER taskVector machineEntity recognitionNatural language processing researchSequential labeling algorithmClinical entity recognitionLarge margin theoryClinical text processingLanguage processing researchPerformance of CRFsHighest F-measureClinical NLP researchI2b2 NLP challengeSame feature setsBetter performance
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
2011
Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases.
Xu H, Fu Z, Shah A, Chen Y, Peterson N, Chen Q, Mani S, Levy M, Dai Q, Denny J. Extracting and integrating data from entire electronic health records for detecting colorectal cancer cases. AMIA Annual Symposium Proceedings 2011, 2011: 1564-72. PMID: 22195222, PMCID: PMC3243156.Peer-Reviewed Original ResearchConceptsEntire electronic health recordElectronic health recordsNatural language processingHealth recordsStructured EHR dataMachine learningText dataNarrative text dataF-measureLanguage processingClinical narrativesEHR dataSuch tasksColorectal cancerDetection methodConcept identificationCohort of patientsColorectal cancer casesVanderbilt University HospitalCase detection methodsClinical notesCRC patientsCRC casesUniversity HospitalCancer cases
2006
Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues
Xu H, Markatou M, Dimova R, Liu H, Friedman C. Machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues. BMC Bioinformatics 2006, 7: 334. PMID: 16822321, PMCID: PMC1550263, DOI: 10.1186/1471-2105-7-334.Peer-Reviewed Original ResearchConceptsNatural language processingBiomedical domainInformation retrieval systemsML methodsWSD classifierSense disambiguationMachine learning methodsVector machine classifierError rateWord sense disambiguationRetrieval systemMachine learningML techniquesText miningBiomedical abbreviationsLanguage processingLearning methodsCross-validation methodWSD problemMachine classifierAccurate accessSense distributionClassifierBiomolecular entitiesWSD task