2020
Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events
Li F, Du J, He Y, Song H, Madkour M, Rao G, Xiang Y, Luo Y, Chen H, Liu S, Wang L, Liu H, Xu H, Tao C. Time event ontology (TEO): to support semantic representation and reasoning of complex temporal relations of clinical events. Journal Of The American Medical Informatics Association 2020, 27: 1046-1056. PMID: 32626903, PMCID: PMC7647306, DOI: 10.1093/jamia/ocaa058.Peer-Reviewed Original ResearchMeSH KeywordsBiological OntologiesDecision Support Systems, ClinicalElectronic Health RecordsHumansNatural Language ProcessingSemantic WebTimeConceptsTime Event OntologyComplex temporal relationsEvent ontologyNatural language processing fieldTemporal relationsTime-related queriesInformation annotationProcessing fieldTemporal informationData propertiesRelation representationClinical narrativesSemantic representationElectronic health record dataRich setHealth record dataOntologyStrong capabilityReasoningSetQueriesOrder relationRecord dataRepresentationPrimitives
2011
Applying active learning to assertion classification of concepts in clinical text
Chen Y, Mani S, Xu H. Applying active learning to assertion classification of concepts in clinical text. Journal Of Biomedical Informatics 2011, 45: 265-272. PMID: 22127105, PMCID: PMC3306548, DOI: 10.1016/j.jbi.2011.11.003.Peer-Reviewed Original ResearchData MiningDecision Support Systems, ClinicalHumansNatural Language ProcessingProblem-Based LearningSemanticsA study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries
Jiang M, Chen Y, Liu M, Rosenbloom S, Mani S, Denny J, Xu H. A study of machine-learning-based approaches to extract clinical entities and their assertions from discharge summaries. Journal Of The American Medical Informatics Association 2011, 18: 601-606. PMID: 21508414, PMCID: PMC3168315, DOI: 10.1136/amiajnl-2011-000163.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceData MiningDecision Support Systems, ClinicalElectronic Health RecordsHumansNatural Language ProcessingPatient DischargePattern Recognition, AutomatedSemanticsVocabulary, ControlledConceptsEntity extraction systemCenter of InformaticsConcept extractionIntegrating BiologyEntity recognition moduleEntity recognition systemConditional Random FieldsOverall F-scoreSupport vector machineRule-based moduleAssertion classificationClassification taskRecognition moduleRecognition systemML algorithmsSemantic informationTraining dataClinical textNatural languageF-measureChallenge organizersF-scoreVector machineEvaluation scriptsTraining corpus
2010
Extracting timing and status descriptors for colonoscopy testing from electronic medical records
Denny J, Peterson J, Choma N, Xu H, Miller R, Bastarache L, Peterson N. Extracting timing and status descriptors for colonoscopy testing from electronic medical records. Journal Of The American Medical Informatics Association 2010, 17: 383-388. PMID: 20595304, PMCID: PMC2995656, DOI: 10.1136/jamia.2010.004804.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsColonoscopyData MiningDecision Support Systems, ClinicalElectronic Health RecordsHumansMiddle AgedNatural Language ProcessingSoftware ValidationTennesseeConceptsElectronic medical recordsMedical recordsColorectal cancer screening ratesCRC screening statusCancer screening ratesManual reviewStatus indicatorsHealth services researchersColonoscopy testingEMR notesTypes of CRCScreening statusScreening ratesColonoscopy screeningBilling codesUseful adjunctGold standardElectronic recordsColonoscopyPatientsServices researchersFurther investigationRandom sampleTemporal expression