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 informationA 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 issues
2023
Social Determinants, Cardiovascular Disease, and Health Care Cost: A Nationwide Study in the United States Using Machine Learning
Sun F, Yao J, Du S, Qian F, Appleton A, Tao C, Xu H, Liu L, Dai Q, Joyce B, Nannini D, Hou L, Zhang K. Social Determinants, Cardiovascular Disease, and Health Care Cost: A Nationwide Study in the United States Using Machine Learning. Journal Of The American Heart Association 2023, 12: e027919. PMID: 36802713, PMCID: PMC10111459, DOI: 10.1161/jaha.122.027919.Peer-Reviewed Original Research
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 ResearchMeSH KeywordsDatasets as TopicHumansInformation Storage and RetrievalMachine LearningNarrationNatural Language ProcessingSemanticsConceptsPre-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
Recognizing software names in biomedical literature using machine learning
Wei Q, Zhang Y, Amith M, Lin R, Lapeyrolerie J, Tao C, Xu H. Recognizing software names in biomedical literature using machine learning. Health Informatics Journal 2019, 26: 21-33. PMID: 31566474, PMCID: PMC7334865, DOI: 10.1177/1460458219869490.Peer-Reviewed Original ResearchMeSH KeywordsBiomedical TechnologyKnowledge DiscoveryMachine LearningNatural Language ProcessingPublicationsSoftwareConceptsSoftware namesF-measureNatural language processing methodsBiomedical literatureWord representation featuresLanguage processing methodsEntity recognition systemSoftware catalogSoftware repositoriesFeature engineeringBiomedical softwareRecognition systemSoftware toolsBiomedical domainRepresentation featuresMEDLINE abstractsWord embeddingsKnowledge featuresManual curationSoftwareMachineProcessing methodsBest systemRepositorySystemA study of deep learning approaches for medication and adverse drug event extraction from clinical text
Wei Q, Ji Z, Li Z, Du J, Wang J, Xu J, Xiang Y, Tiryaki F, Wu S, Zhang Y, Tao C, Xu H. A study of deep learning approaches for medication and adverse drug event extraction from clinical text. Journal Of The American Medical Informatics Association 2019, 27: 13-21. PMID: 31135882, PMCID: PMC6913210, DOI: 10.1093/jamia/ocz063.Peer-Reviewed Original ResearchConceptsDeep learning-based approachDeep learning approachLearning-based approachTraditional machineLearning approachNational NLP Clinical ChallengesAdverse drug event extractionOutperform traditional machineDifferent ensemble approachesConditional Random FieldsSequence labeling approachMIMIC-III databaseEvent extractionMedical domainEntity recognitionClassification componentF1 scoreClinical textRelation extractionClinical documentsVector machineEnd evaluationEnsemble approachClinical corpusMachine
2018
Psychiatric stressor recognition from clinical notes to reveal association with suicide
Zhang Y, Zhang O, Li R, Flores A, Selek S, Zhang X, Xu H. Psychiatric stressor recognition from clinical notes to reveal association with suicide. Health Informatics Journal 2018, 25: 1846-1862. PMID: 30328378, DOI: 10.1177/1460458218796598.Peer-Reviewed Original ResearchConceptsElectronic health recordsSuicidal behaviorHealth recordsSuicide ideation/attemptsTremendous economic burdenPsychiatric stressorsSuicide risk factorsRisk factorsEconomic burdenPsychiatric stressClinical notesLarge-scale studiesPsychiatric notesSuicideAssociationSignificant stressorsStressorsPrior studiesPercentPrevious studiesStudyExtraction of BI-RADS findings from breast ultrasound reports in Chinese using deep learning approaches
Miao S, Xu T, Wu Y, Xie H, Wang J, Jing S, Zhang Y, Zhang X, Yang Y, Zhang X, Shan T, Wang L, Xu H, Wang S, Liu Y. Extraction of BI-RADS findings from breast ultrasound reports in Chinese using deep learning approaches. International Journal Of Medical Informatics 2018, 119: 17-21. PMID: 30342682, DOI: 10.1016/j.ijmedinf.2018.08.009.Peer-Reviewed Original ResearchConceptsLearning-based methodsBreast ultrasound reportsElectronic health record systemsTraditional machine learning-based methodsDeep learning-based approachDeep learning-based methodsNatural language processing methodsMachine learning-based methodsDeep learning technologyConditional random field algorithmDeep learning approachLanguage processing methodsLearning-based approachUltrasound reportsBreast cancer researchRule-based methodHealth record systemsBreast radiology reportsLearning technologyNLP approachLearning approachField algorithmDetailed clinical informationWide adoptionRecord systemLeveraging existing corpora for de-identification of psychiatric notes using domain adaptation.
Lee H, Zhang Y, Roberts K, Xu H. Leveraging existing corpora for de-identification of psychiatric notes using domain adaptation. AMIA Annual Symposium Proceedings 2018, 2017: 1070-1079. PMID: 29854175, PMCID: PMC5977650.Peer-Reviewed Original ResearchInteractive medical word sense disambiguation through informed learning
Wang Y, Zheng K, Xu H, Mei Q. Interactive medical word sense disambiguation through informed learning. Journal Of The American Medical Informatics Association 2018, 25: 800-808. PMID: 29584896, PMCID: PMC6658868, DOI: 10.1093/jamia/ocy013.Peer-Reviewed Original Research
2017
Entity recognition from clinical texts via recurrent neural network
Liu Z, Yang M, Wang X, Chen Q, Tang B, Wang Z, Xu H. Entity recognition from clinical texts via recurrent neural network. BMC Medical Informatics And Decision Making 2017, 17: 67. PMID: 28699566, PMCID: PMC5506598, DOI: 10.1186/s12911-017-0468-7.Peer-Reviewed Original ResearchMeSH KeywordsElectronic Health RecordsHumansMachine LearningMedical InformaticsNatural Language ProcessingNeural Networks, ComputerConceptsRecurrent neural networkNatural language processingEntity recognitionClinical textTraditional machineNeural networkClinical natural language processingMedical concept extractionHand-crafted featuresClinical entity recognitionDeep learning methodsClinical event detectionConditional Random FieldsSupport vector machineI2b2 NLP challengePerformance of LSTMTypes of entitiesClinical domainsContext informationFeature engineeringConcept extractionDe-identificationEvent detectionKnowledge basesLSTM layersIdentification 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 SystemClinical Word Sense Disambiguation with Interactive Search and Classification.
Wang Y, Zheng K, Xu H, Mei Q. Clinical Word Sense Disambiguation with Interactive Search and Classification. AMIA Annual Symposium Proceedings 2017, 2016: 2062-2071. PMID: 28269966, PMCID: PMC5333264.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsHumansMachine LearningModels, TheoreticalNatural Language ProcessingProblem-Based LearningConceptsDomain knowledgeHuman expertsWSD modelClinical textCurrent active learning methodsWord sense disambiguation systemNatural language processing applicationsMachine learning processLanguage processing applicationsWord sense disambiguationActive learning methodsContextual wordsInteractive searchWord ambiguityLearning methodsSense disambiguationProcessing applicationsAmbiguous instancesSearch processDisambiguation systemEvaluation corpusLearning processExpertsQueriesClassifier
2016
A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD)
Wu Y, Denny J, Rosenbloom S, Miller R, Giuse D, Wang L, Blanquicett C, Soysal E, Xu J, Xu H. A long journey to short abbreviations: developing an open-source framework for clinical abbreviation recognition and disambiguation (CARD). Journal Of The American Medical Informatics Association 2016, 24: e79-e86. PMID: 27539197, PMCID: PMC7651947, DOI: 10.1093/jamia/ocw109.Peer-Reviewed Original ResearchMeSH KeywordsAbbreviations as TopicElectronic Health RecordsHumansMachine LearningNatural Language ProcessingPatient DischargeConceptsClinical NLP systemsOpen-source frameworkNLP systemsClinical corpusClinical abbreviationsClinic visit notesSense inventoryKnowledge Extraction SystemAbbreviation recognitionWord sense disambiguation methodDischarge summariesF1 scoreExternal corpusClinical narrativesSense disambiguation methodSystem capabilitiesVanderbilt University Medical CenterWrapperFrequent abbreviationsDisambiguation methodMetaMapAbbreviation identificationCardsVisit notesDisambiguationExtracting 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
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 ResearchMeSH KeywordsAlgorithmsHumansMachine LearningNatural Language ProcessingPattern Recognition, AutomatedSemanticsA 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 textNamed 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