2024
Standardizing Multi-site Clinical Note Titles to LOINC Document Ontology: A Transformer-based Approach.
Zuo X, Zhou Y, Duke J, Hripcsak G, Shah N, Banda J, Reeves R, Miller T, Waitman L, Natarajan K, Xu H. Standardizing Multi-site Clinical Note Titles to LOINC Document Ontology: A Transformer-based Approach. AMIA Annual Symposium Proceedings 2024, 2023: 834-843. PMID: 38222429, PMCID: PMC10785935.Peer-Reviewed Original Research
2023
Systematic design and data-driven evaluation of social determinants of health ontology (SDoHO).
Dang Y, Li F, Hu X, Keloth V, Zhang M, Fu S, Amith M, Fan J, Du J, Yu E, Liu H, Jiang X, Xu H, Tao C. Systematic design and data-driven evaluation of social determinants of health ontology (SDoHO). Journal Of The American Medical Informatics Association 2023, 30: 1465-1473. PMID: 37301740, PMCID: PMC10436148, DOI: 10.1093/jamia/ocad096.Peer-Reviewed Original Research
2022
Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature
Schutte D, Vasilakes J, Bompelli A, Zhou Y, Fiszman M, Xu H, Kilicoglu H, Bishop J, Adam T, Zhang R. Discovering novel drug-supplement interactions using SuppKG generated from the biomedical literature. Journal Of Biomedical Informatics 2022, 131: 104120. PMID: 35709900, PMCID: PMC9335448, DOI: 10.1016/j.jbi.2022.104120.Peer-Reviewed Original ResearchMeSH KeywordsDietary SupplementsNatural Language ProcessingPubMedSemanticsUnified Medical Language SystemConceptsUnified Medical Language SystemComprehensive knowledge graphDomain terminologyKnowledge graphSemantic relationsNatural language processing technologyLanguage processing technologyNLP toolsDownstream tasksF1 scoreSemantic relationshipsDiscovery patternsPubMed abstractsLimited coverageBiomedical literatureProcessing technologyLanguage systemSemRepDietary supplement informationManual reviewNovel methodologyGraphNodesDomainTask
2020
Relation Extraction from Clinical Narratives Using Pre-trained Language Models.
Wei Q, Ji Z, Si Y, Du J, Wang J, Tiryaki F, Wu S, Tao C, Roberts K, Xu H. Relation Extraction from Clinical Narratives Using Pre-trained Language Models. AMIA Annual Symposium Proceedings 2020, 2019: 1236-1245. PMID: 32308921, PMCID: PMC7153059.Peer-Reviewed Original ResearchConceptsPre-trained language modelsNatural language processingLanguage modelRE tasksNLP tasksClinical narrativesRecent deep learning methodsDeep learning methodsClinical NLP tasksRelation extraction taskTraditional word embeddingsTraditional machineExtraction taskArt performanceRelation extractionBERT modelLanguage processingLearning methodsWord embeddingsShared TaskPrevious stateBiomedical literatureDifferent implementationsTaskOpen domain
2019
Ontological representation–oriented term normalization and standardization of the Research Domain Criteria
Li F, Rao G, Du J, Xiang Y, Zhang Y, Selek S, Hamilton J, Xu H, Tao C. Ontological representation–oriented term normalization and standardization of the Research Domain Criteria. Health Informatics Journal 2019, 26: 726-737. PMID: 30843449, PMCID: PMC7863676, DOI: 10.1177/1460458219832059.Peer-Reviewed Original Research
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
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 resultsInformationDatasetSystemPsychiatric symptom recognition without labeled data using distributional representations of phrases and on-line knowledge
Zhang Y, Zhang O, Wu Y, Lee H, Xu J, Xu H, Roberts K. Psychiatric symptom recognition without labeled data using distributional representations of phrases and on-line knowledge. Journal Of Biomedical Informatics 2017, 75: s129-s137. PMID: 28624644, PMCID: PMC5705397, DOI: 10.1016/j.jbi.2017.06.014.Peer-Reviewed Original ResearchConceptsPsychiatric symptomsCandidate symptomMental disordersPsychiatric notesList of symptomsMayo ClinicClinical dataSymptom recognitionPatient experiencePersonalized preventionSymptomsAmerican Psychiatric AssociationAbstractTextClinical conceptsPsychiatric AssociationPhenotypic classificationDisordersClinical textMIMIC-IIHealthcare knowledgeConclusionSubjective descriptionsClinicDiseaseDiagnosisSemantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features.
Zhang Y, Jiang M, Wang J, Xu H. Semantic Role Labeling of Clinical Text: Comparing Syntactic Parsers and Features. AMIA Annual Symposium Proceedings 2017, 2016: 1283-1292. PMID: 28269926, PMCID: PMC5333340.Peer-Reviewed Original Research
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 precisionInformationSparsenessGraphAutomated identification of molecular effects of drugs (AIMED)
Fathiamini S, Johnson A, Zeng J, Araya A, Holla V, Bailey A, Litzenburger B, Sanchez N, Khotskaya Y, Xu H, Meric-Bernstam F, Bernstam E, Cohen T. Automated identification of molecular effects of drugs (AIMED). Journal Of The American Medical Informatics Association 2016, 23: 758-765. PMID: 27107438, PMCID: PMC4926748, DOI: 10.1093/jamia/ocw030.Peer-Reviewed Original Research
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 ResearchA Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text.
Wu Y, Xu J, Jiang M, Zhang Y, Xu H. A Study of Neural Word Embeddings for Named Entity Recognition in Clinical Text. AMIA Annual Symposium Proceedings 2015, 2015: 1326-33. PMID: 26958273, PMCID: PMC4765694.Peer-Reviewed Original ResearchConceptsNamed Entity RecognitionClinical NER systemNeural word embeddingsClinical Named Entity RecognitionWord embeddingsNER systemWord representationsI2b2 dataEntity recognitionEmbedding featuresClinical textNatural language processing researchConditional Random FieldsLanguage processing researchWord embedding featuresLarge unlabeled corpusBrown clustersNeural wordImportant patient informationFeature representationF1 scoreIntelligent monitoringCritical taskUnlabeled corpusSemantic relationsDomain adaptation for semantic role labeling of clinical text
Zhang Y, Tang B, Jiang M, Wang J, Xu H. Domain adaptation for semantic role labeling of clinical text. Journal Of The American Medical Informatics Association 2015, 22: 967-979. PMID: 26063745, PMCID: PMC4986662, DOI: 10.1093/jamia/ocu048.Peer-Reviewed Original Research
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
Recognizing 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 performanceAnalyzing differences between chinese and english clinical text: a cross-institution comparison of discharge summaries in two languages.
Wu Y, Lei J, Wei W, Tang B, Denny J, Rosenbloom S, Miller R, Giuse D, Zheng K, Xu H. Analyzing differences between chinese and english clinical text: a cross-institution comparison of discharge summaries in two languages. 2013, 192: 662-6. PMID: 23920639, PMCID: PMC4957806.Peer-Reviewed Original ResearchConceptsNatural language processing toolsEnglish clinical textClinical textLanguage processing toolsChinese clinical textCultural differencesMajor clinical componentsTextWestern institutionsInpatient discharge summariesCross-country collaborationDocument levelProcessing toolsClinical documentsLanguageUS institutionsUsesUnprecedented amountValuable insightsInstitutionsDocumentsChinaWorldwide adoptionEMR dataCollaboration
2012
Extracting semantic lexicons from discharge summaries using machine learning and the C-Value method.
Jiang M, Denny J, Tang B, Cao H, Xu H. Extracting semantic lexicons from discharge summaries using machine learning and the C-Value method. AMIA Annual Symposium Proceedings 2012, 2012: 409-16. PMID: 23304311, PMCID: PMC3540581.Peer-Reviewed Original ResearchRecognition of medication information from discharge summaries using ensembles of classifiers
Doan S, Collier N, Xu H, Duy P, Phuong T. Recognition of medication information from discharge summaries using ensembles of classifiers. BMC Medical Informatics And Decision Making 2012, 12: 36. PMID: 22564405, PMCID: PMC3502425, DOI: 10.1186/1472-6947-12-36.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceDecision Support TechniquesFemaleHumansInformation Storage and RetrievalInstitutional Management TeamsMaleMedication SystemsNatural Language ProcessingPatient DischargePattern Recognition, AutomatedPharmaceutical PreparationsReproducibility of ResultsSemanticsSoftware DesignSupport Vector MachineConceptsConditional Random FieldsNatural language processingClinical natural language processingSupport vector machineBest F-scoreEnsemble classifierF-scoreClinical textIndividual classifiersVoting methodMajority votingLocal support vector machineSupervised machine learning methodsClinical entity recognitionClinical NLP systemsDifferent voting strategiesEntity recognition systemRule-based systemEnsemble of classifiersMachine learning methodsRule-based methodI2b2 NLP challengeEntity recognitionRecognition systemNLP systems