2024
Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition
Zuo X, Kumar A, Shen S, Li J, Cong G, Jin E, Chen Q, Warner J, Yang P, Xu H. Extracting Systemic Anticancer Therapy and Response Information From Clinical Notes Following the RECIST Definition. JCO Clinical Cancer Informatics 2024, 8: e2300166. PMID: 38885475, DOI: 10.1200/cci.23.00166.Peer-Reviewed Original ResearchConceptsNatural language processingDomain-specific language modelsNatural language processing systemsInformation extraction systemRule-based moduleNarrative clinical textsNLP tasksEntity recognitionText normalizationAssertion classificationLanguage modelInformation extractionClinical textElectronic health recordsLearning-basedClinical notesLanguage processingTest setSystem performanceHealth recordsResponse extractionTime-consumingAnticancer therapyInformationAssessment information
2019
Extracting entities with attributes in clinical text via joint deep learning
Shi X, Yi Y, Xiong Y, Tang B, Chen Q, Wang X, Ji Z, Zhang Y, Xu H. Extracting entities with attributes in clinical text via joint deep learning. Journal Of The American Medical Informatics Association 2019, 26: 1584-1591. PMID: 31550346, PMCID: PMC7647140, DOI: 10.1093/jamia/ocz158.Peer-Reviewed Original ResearchMeSH KeywordsData MiningDatasets as TopicDeep LearningElectronic Health RecordsHumansNatural Language ProcessingConceptsBidirectional long short-term memoryShort-term memoryLong short-term memoryNatural language processingEntity recognitionChinese corpusBest F1English corpusLanguage processingJoint deep learningTaskConditional Random FieldsRelation extractionAttribute recognitionMemorySequential subtasksDeep learning methodsClinical textDiscovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing
Wu Y, Warner J, Wang L, Jiang M, Xu J, Chen Q, Nian H, Dai Q, Du X, Yang P, Denny J, Liu H, Xu H. Discovery of Noncancer Drug Effects on Survival in Electronic Health Records of Patients With Cancer: A New Paradigm for Drug Repurposing. JCO Clinical Cancer Informatics 2019, 3: cci.19.00001. PMID: 31141421, PMCID: PMC6693869, DOI: 10.1200/cci.19.00001.Peer-Reviewed Original ResearchConceptsVanderbilt University Medical CenterCancer survivalMayo ClinicDrug repurposingNoncancer drugsElectronic health record dataCancer registry dataEHR dataClinical trial evaluationOverall cancer survivalUniversity Medical CenterHealth record dataElectronic health recordsTreatment of cancerClinical trialsDrug classesRegistry dataMedical CenterDrug effectsSignificant associationLongitudinal EHRNew indicationsPatientsCancerHealth recordsTemporal indexing of medical entity in Chinese clinical notes
Liu Z, Wang X, Chen Q, Tang B, Xu H. Temporal indexing of medical entity in Chinese clinical notes. BMC Medical Informatics And Decision Making 2019, 19: 17. PMID: 30700331, PMCID: PMC6354334, DOI: 10.1186/s12911-019-0735-x.Peer-Reviewed Original ResearchConceptsSupport vector machineConvolutional neural networkTemporal indexingNeural network modelIndexing taskRelation classificationMedical entitiesRecurrent convolutional neural network modelMachine learning-based systemsConvolutional neural network modelDeep neural network modelNetwork methodNetwork modelLearning-based systemTemporal relation classificationRecurrent neural network methodChinese clinical notesTemporal relationsClinical notesNeural network methodI2b2 NLP challengeContext informationTime indexingSemantic informationBaseline methods
2018
Clinical text annotation - what factors are associated with the cost of time?
Wei Q, Franklin A, Cohen T, Xu H. Clinical text annotation - what factors are associated with the cost of time? AMIA Annual Symposium Proceedings 2018, 2018: 1552-1560. PMID: 30815201, PMCID: PMC6371268.Peer-Reviewed Original ResearchConceptsAnnotation timeClinical textNatural language processing modelsClinical corpusIndividual user behaviorEntity recognition taskLanguage processing modelsPractice of annotationCharacteristics of sentencesClinical Text AnnotationText annotationsUser behaviorIndividual usersCost of timeActive learning researchRecognition taskLearning researchProcessing modelCost modelAnnotationUsersLimited workCorpusTextTask
2017
Accurate Identification of Fatty Liver Disease in Data Warehouse Utilizing Natural Language Processing
Redman J, Natarajan Y, Hou J, Wang J, Hanif M, Feng H, Kramer J, Desiderio R, Xu H, El-Serag H, Kanwal F. Accurate Identification of Fatty Liver Disease in Data Warehouse Utilizing Natural Language Processing. Digestive Diseases And Sciences 2017, 62: 2713-2718. PMID: 28861720, DOI: 10.1007/s10620-017-4721-9.Peer-Reviewed Original ResearchConceptsData warehouseFatty liver diseaseLanguage processingNatural language processingLiver diseaseF-measureAlgorithm developmentVeterans Affairs Corporate Data WarehouseMagnetic resonance imaging reportsOutcomes of patientsAlgorithmExpert radiologistsValidation methodElectronic medical recordsCorporate Data WarehouseWarehouseAbdominal ultrasoundManual reviewHepatic steatosisMedical recordsRandom national sampleClinical studiesLarge cohortComputerized tomographyImaging reportsIdentification 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
2016
Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature
Zhang Y, Wu H, Xu J, Wang J, Soysal E, Li L, Xu H. Leveraging syntactic and semantic graph kernels to extract pharmacokinetic drug drug interactions from biomedical literature. BMC Systems Biology 2016, 10: 67. PMID: 27585838, PMCID: PMC5009562, DOI: 10.1186/s12918-016-0311-2.Peer-Reviewed Original ResearchConceptsPaths graph kernelGraph kernelsSemantic classesSemantic informationBiomedical literatureShallow semantic representationsText mining techniquesBest F-scoreAutomatic DDI extractionProblem of sparsenessDependency structureSemantic graphDDI detectionKnowledge basesDDI corpusF-scoreDDI extractionSemantic representationNovel approachExperimental resultsKernelHigh precisionInformationSparsenessGraphChemical named entity recognition in patents by domain knowledge and unsupervised feature learning
Zhang Y, Xu J, Chen H, Wang J, Wu Y, Prakasam M, Xu H. Chemical named entity recognition in patents by domain knowledge and unsupervised feature learning. Database 2016, 2016: baw049. PMID: 27087307, PMCID: PMC4834204, DOI: 10.1093/database/baw049.Peer-Reviewed Original ResearchConceptsMachine learning-based systemsLearning-based systemConditional Random FieldsDomain knowledgeEntity recognitionMatthews correlation coefficientDrug Named Entity RecognitionBioCreative V challengeInformation extraction systemWord representation featuresUnsupervised feature learningUnsupervised learning algorithmNamed Entity RecognitionSemantic type informationSupport vector machinePrecision-recall curveBrown clusteringFeature learningFeature engineeringUnsupervised featureIndividual subtasksMining systemNER taskLearning algorithmCPD taskCD-REST: a system for extracting chemical-induced disease relation in literature
Xu J, Wu Y, Zhang Y, Wang J, Lee H, Xu H. CD-REST: a system for extracting chemical-induced disease relation in literature. Database 2016, 2016: baw036. PMID: 27016700, PMCID: PMC4808251, DOI: 10.1093/database/baw036.Peer-Reviewed Original ResearchConceptsChemical-induced disease relationsWeb servicesBiomedical literatureEntity recognitionMachine learning-based approachLearning-based approachHTTP POST requestRelation extraction systemVector space modelConditional Random FieldsSupport vector machineRelation extraction moduleVast biomedical literatureDisease relation extractionChemical-induced disease relation extractionExtraction moduleDisease relationsAutomatic extractionEnd systemPOST requestRelation extractionNormalization moduleVector machineBioCreative VDemonstration systemExtracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov
Xu J, Lee H, Zeng J, Wu Y, Zhang Y, Huang L, Johnson A, Holla V, Bailey A, Cohen T, Meric-Bernstam F, Bernstam E, Xu H. Extracting genetic alteration information for personalized cancer therapy from ClinicalTrials.gov. Journal Of The American Medical Informatics Association 2016, 23: 750-757. PMID: 27013523, PMCID: PMC4926744, DOI: 10.1093/jamia/ocw009.Peer-Reviewed Original ResearchExtracting drug-enzyme relation from literature as evidence for drug drug interaction
Zhang Y, Wu H, Du J, Xu J, Wang J, Tao C, Li L, Xu H. Extracting drug-enzyme relation from literature as evidence for drug drug interaction. Journal Of Biomedical Semantics 2016, 7: 11. PMID: 26955465, PMCID: PMC4780188, DOI: 10.1186/s13326-016-0052-6.Peer-Reviewed Original Research
2015
Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2
Stubbs A, Kotfila C, Xu H, Uzuner Ö. Identifying risk factors for heart disease over time: Overview of 2014 i2b2/UTHealth shared task Track 2. Journal Of Biomedical Informatics 2015, 58: s67-s77. PMID: 26210362, PMCID: PMC4978189, DOI: 10.1016/j.jbi.2015.07.001.Peer-Reviewed Original ResearchMeSH KeywordsAgedBostonCohort StudiesComorbidityComputer SecurityConfidentialityCoronary Artery DiseaseData MiningDiabetes ComplicationsElectronic Health RecordsFemaleHumansIncidenceLongitudinal StudiesMaleMiddle AgedNarrationNatural Language ProcessingPattern Recognition, AutomatedRisk AssessmentVocabulary, ControlledConceptsCoronary artery diseaseRisk factorsLongitudinal medical recordsMedical recordsMedical risk factorsArtery diseaseDiabetic patientsSmoking statusHeart diseaseFamily historyI2b2/UTHealth natural language processingDiseaseI2b2/UTHealthProgressionUTHealthHypertensionHyperlipidemiaFactorsObesityDiabetesPatientsEase of adoption of clinical natural language processing software: An evaluation of five systems
Zheng K, Vydiswaran V, Liu Y, Wang Y, Stubbs A, Uzuner Ö, Gururaj A, Bayer S, Aberdeen J, Rumshisky A, Pakhomov S, Liu H, Xu H. Ease of adoption of clinical natural language processing software: An evaluation of five systems. Journal Of Biomedical Informatics 2015, 58: s189-s196. PMID: 26210361, PMCID: PMC4974203, DOI: 10.1016/j.jbi.2015.07.008.Peer-Reviewed Original ResearchConceptsClinical NLP systemsNLP systemsNatural language processing softwareThird-party componentsUsability testing toolGroup of usersLanguage processing softwareEase of adoptionExpert evaluatorsSoftware distributionBiomedical softwareComputer scienceEnd usersUsability assessmentI2b2 challengeTesting toolsEvaluation showHuman evaluatorsSystem submissionsEase of useHealth informaticsProcessing softwareAdoption issuesUsersSpecial trackUsing Ontology Fingerprints to disambiguate gene name entities in the biomedical literature
Chen G, Zhao J, Cohen T, Tao C, Sun J, Xu H, Bernstam E, Lawson A, Zeng J, Johnson A, Holla V, Bailey A, Lara-Guerra H, Litzenburger B, Meric-Bernstam F, Zheng W. Using Ontology Fingerprints to disambiguate gene name entities in the biomedical literature. Database 2015, 2015: bav034. PMID: 25858285, PMCID: PMC4390608, DOI: 10.1093/database/bav034.Peer-Reviewed Original ResearchNamed Entity Recognition in Chinese Clinical Text Using Deep Neural Network.
Wu Y, Jiang M, Lei J, Xu H. Named Entity Recognition in Chinese Clinical Text Using Deep Neural Network. 2015, 216: 624-8. PMID: 26262126, PMCID: PMC4624324.Peer-Reviewed Original ResearchConceptsDeep neural networksLarge unlabeled corpusNamed Entity RecognitionWord embeddingsUnlabeled corpusUnsupervised learningEntity recognitionNeural networkNatural language processing technologyNovel deep learning methodLanguage processing technologyDeep learning methodsUnsupervised feature learningFeature engineering approachImportant healthcare informationChinese clinical textTypes of entitiesFeature learningNER taskClinical textLearning methodsClinical documentsCRF modelHealthcare informationFree text
2014
Identifying plausible adverse drug reactions using knowledge extracted from the literature
Shang N, Xu H, Rindflesch T, Cohen T. Identifying plausible adverse drug reactions using knowledge extracted from the literature. Journal Of Biomedical Informatics 2014, 52: 293-310. PMID: 25046831, PMCID: PMC4261011, DOI: 10.1016/j.jbi.2014.07.011.Peer-Reviewed Original ResearchConceptsPredication-based Semantic IndexingReflective Random IndexingLBD methodsNatural language processing toolsBiomedical literatureDrug-adverse event associationsLanguage processing toolsSemantic indexingElectronic health recordsRandom IndexingHuman reviewVast repositoryDiscovery methodsVolume of knowledgeProcessing toolsEvaluation setHealth recordsData sourcesEvent associationsIndexingDrug-effect relationshipsRepositoryLarge volumesADR associationsReasoning pathwaysEvaluating Word Representation Features in Biomedical Named Entity Recognition Tasks
Tang B, Cao H, Wang X, Chen Q, Xu H. Evaluating Word Representation Features in Biomedical Named Entity Recognition Tasks. BioMed Research International 2014, 2014: 240403. PMID: 24729964, PMCID: PMC3963372, DOI: 10.1155/2014/240403.Peer-Reviewed Original ResearchConceptsBiomedical Named Entity RecognitionWord representationsNamed Entity Recognition (NER) taskMachine learning-based approachWord representation featuresNatural language processingLearning-based approachEntity recognition taskNamed Entity RecognitionCluster-based representationJNLPBA corpusEntity recognitionBiomedical domainF-measureLanguage processingRepresentation featuresWord embeddingsRecognition taskWR algorithmDistributional representationsTaskBetter performanceAlgorithmRepresentationDifferent types
2013
Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data
Denny J, Bastarache L, Ritchie M, Carroll R, Zink R, Mosley J, Field J, Pulley J, Ramirez A, Bowton E, Basford M, Carrell D, Peissig P, Kho A, Pacheco J, Rasmussen L, Crosslin D, Crane P, Pathak J, Bielinski S, Pendergrass S, Xu H, Hindorff L, Li R, Manolio T, Chute C, Chisholm R, Larson E, Jarvik G, Brilliant M, McCarty C, Kullo I, Haines J, Crawford D, Masys D, Roden D. Systematic comparison of phenome-wide association study of electronic medical record data and genome-wide association study data. Nature Biotechnology 2013, 31: 1102-1111. PMID: 24270849, PMCID: PMC3969265, DOI: 10.1038/nbt.2749.Peer-Reviewed Original Research
2011
RANKING GENE-DRUG RELATIONSHIPS IN BIOMEDICAL LITERATURE USING LATENT DIRICHLET ALLOCATION
Altman R, Dunker A, Hunter L, Murray T, Klein T, WU Y, LIU M, ZHENG W, ZHAO Z, XU H. RANKING GENE-DRUG RELATIONSHIPS IN BIOMEDICAL LITERATURE USING LATENT DIRICHLET ALLOCATION. Biocomputing 2011, 422-33. PMID: 22174297, PMCID: PMC4095990, DOI: 10.1142/9789814366496_0041.Peer-Reviewed Original Research