2019
Enhancing clinical concept extraction with contextual embeddings
Si Y, Wang J, Xu H, Roberts K. Enhancing clinical concept extraction with contextual embeddings. Journal Of The American Medical Informatics Association 2019, 26: 1297-1304. PMID: 31265066, PMCID: PMC6798561, DOI: 10.1093/jamia/ocz096.Peer-Reviewed Original ResearchConceptsClinical concept extractionContextual embeddingsNatural language processing tasksTraditional word embeddingsTraditional word representationsClinical NLP tasksLanguage processing tasksSemantic informationWord embedding methodsLarge language modelsArt performanceConcept extraction taskSemEval 2014Word representationsNLP tasksLanguage modelWord embeddingsProcessing tasksNeural network-based representationI2b2 2010Concept extractionTaskLarge clinical corpusClinical corpusNetwork-based representationTemporal 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
2017
CNN-based ranking for biomedical entity normalization
Li H, Chen Q, Tang B, Wang X, Xu H, Wang B, Huang D. CNN-based ranking for biomedical entity normalization. BMC Bioinformatics 2017, 18: 385. PMID: 28984180, PMCID: PMC5629610, DOI: 10.1186/s12859-017-1805-7.Peer-Reviewed Original ResearchConceptsBiomedical entity normalizationEntity normalizationSemantic informationCNN architectureNovel convolutional neural network architectureConvolutional neural network architectureTraditional rule-based methodsNeural network architectureRule-based systemRanking methodRule-based methodNetwork architectureBiomedical entitiesBenchmark datasetsArt performanceEntity mentionsRanking problemCNNNormalization systemArchitectureMorphological informationComparison resultsInformationDatasetSystemKnowledge-Based Approach for Named Entity Recognition in Biomedical Literature: A Use Case in Biomedical Software Identification
Amith M, Zhang Y, Xu H, Tao C. Knowledge-Based Approach for Named Entity Recognition in Biomedical Literature: A Use Case in Biomedical Software Identification. Lecture Notes In Computer Science 2017, 10351: 386-395. DOI: 10.1007/978-3-319-60045-1_40.Peer-Reviewed Original ResearchEntity recognitionNatural language processingContextual semantic informationNamed Entity RecognitionEntity recognition methodFeatures of ontologyMachine learning approachesKnowledge-based approachSoftware entitiesSoftware namesInformation extractionUse casesBiomedical softwareSemantic informationSoftware identificationLanguage processingRecognition methodLearning approachBiomedical literatureRecognitionOntologyEntitiesSoftwareResearch abstractsTask
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 precisionInformationSparsenessGraph
2011
A 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 ResearchConceptsEntity 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