2024
Medical Concept Normalization
Xu H, Demner Fushman D, Hong N, Raja K. Medical Concept Normalization. Cognitive Informatics In Biomedicine And Healthcare 2024, 137-164. DOI: 10.1007/978-3-031-55865-8_6.Peer-Reviewed Original ResearchConcept normalizationDeep learning-based techniquesMedical concept normalizationLearning-based techniquesContemporary machine learningRule-based methodologyAnnotated corpusNLP systemsMachine learningComputing applicationsBiomedical terminologiesNormalization approachStandardized terminologyOntologyTaskLearning
2023
Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration
Yu P, Xu H, Hu X, Deng C. Leveraging Generative AI and Large Language Models: A Comprehensive Roadmap for Healthcare Integration. Healthcare 2023, 11: 2776. PMID: 37893850, PMCID: PMC10606429, DOI: 10.3390/healthcare11202776.Peer-Reviewed Original ResearchLarge language modelsGenerative artificial intelligenceArtificial intelligenceLanguage modelInformation retrievalAI systemsShot learningData managementHuman feedbackReinforcement learningInformation managementSystem implementationCo-design processData acquisitionComprehensive roadmapDecision-making processLearningTechnologyFull potentialHealthcareIntelligenceHealthcare qualityRetrievalIntegrationPromising advancement
2019
Deep learning in clinical natural language processing: a methodical review
Wu S, Roberts K, Datta S, Du J, Ji Z, Si Y, Soni S, Wang Q, Wei Q, Xiang Y, Zhao B, Xu H. Deep learning in clinical natural language processing: a methodical review. Journal Of The American Medical Informatics Association 2019, 27: 457-470. PMID: 31794016, PMCID: PMC7025365, DOI: 10.1093/jamia/ocz200.Peer-Reviewed Original ResearchConceptsNatural language processingClinical natural language processingDeep learningLanguage processingComputing Machinery Digital LibraryInformation extraction tasksMedical informatics communityComputational Linguistics anthologyRecurrent neural networkDigital librariesText classificationElectronic health recordsExtraction taskEntity recognitionWord2vec embeddingsNeural networkRelation extractionNLP communityNLP researchInformatics communitySpecific tasksHealth recordsNLP problemLearningClinical domainsCost-aware active learning for named entity recognition in clinical text
Wei Q, Chen Y, Salimi M, Denny J, Mei Q, Lasko T, Chen Q, Wu S, Franklin A, Cohen T, Xu H. Cost-aware active learning for named entity recognition in clinical text. Journal Of The American Medical Informatics Association 2019, 26: 1314-1322. PMID: 31294792, PMCID: PMC6798575, DOI: 10.1093/jamia/ocz102.Peer-Reviewed Original ResearchConceptsAnnotation costUser studyActive learningAL methodsAL algorithmCost-CAUSEReal-world environmentsAnnotation taskAnnotation timeAnnotation accuracyEntity recognitionClinical textAnnotation dataPassive learningInformative examplesCurve scoreMost approachesSimulation areaUsersSyntactic featuresLearningCost measuresAlgorithmCostAnnotationCost-sensitive Active Learning for Phenotyping of Electronic Health Records.
Ji Z, Wei Q, Franklin A, Cohen T, Xu H. Cost-sensitive Active Learning for Phenotyping of Electronic Health Records. AMIA Joint Summits On Translational Science Proceedings 2019, 2019: 829-838. PMID: 31259040, PMCID: PMC6568101.Peer-Reviewed Original ResearchAnnotation timeElectronic health recordsActive learningMachine learning-based methodsCost-sensitive active learningLarge annotated datasetLearning-based methodsHealth recordsUse casesAnnotated datasetUser 1AL algorithmUser 2Phenotyping algorithmAL approachSecondary useAlgorithmBetter performanceActual timeLearningExperimental resultsBreast cancer patientsDatasetModel performancePassive learning
2017
Interweaving Domain Knowledge and Unsupervised Learning for Psychiatric Stressor Extraction from Clinical Notes
Zhang O, Zhang Y, Xu J, Roberts K, Zhang X, Xu H. Interweaving Domain Knowledge and Unsupervised Learning for Psychiatric Stressor Extraction from Clinical Notes. Lecture Notes In Computer Science 2017, 10351: 396-406. DOI: 10.1007/978-3-319-60045-1_41.Peer-Reviewed Original ResearchNatural language processing systemsWord representation featuresPsychiatric stressorsLanguage processing systemDeep learningDomain knowledgeElectronic health recordsUnsupervised learningInexact matchingClinical notesF-measureRepresentation featuresProcessing systemHealth recordsPsychiatric notesImportant problemMultiple sourcesExperimental resultsLearningAlgorithmChallengesMatchingNarrative textStressor dataRecallInformation retrieval for biomedical datasets: the 2016 bioCADDIE dataset retrieval challenge
Roberts K, Gururaj A, Chen X, Pournejati S, Hersh W, Demner-Fushman D, Ohno-Machado L, Cohen T, Xu H. Information retrieval for biomedical datasets: the 2016 bioCADDIE dataset retrieval challenge. Database 2017, 2017: bax068. DOI: 10.1093/database/bax068.Peer-Reviewed Original ResearchBiomedical datasetsRetrieval challengesInformation retrieval techniquesAdvanced query processingBiomedical data repositoriesAdvanced retrieval methodsQuery processingInformation retrievalTest queriesRetrieval systemRank frameworkRetrieval approachRetrieval techniquesData repositoryRetrieval methodTop precisionDatasetQueriesRepositoryChallengesRetrievalTaskLearningSystemCorpus
2015
Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations
Chen Y, Sun J, Huang L, Xu H, Zhao Z. Classification of Cancer Primary Sites Using Machine Learning and Somatic Mutations. BioMed Research International 2015, 2015: 491502. PMID: 26539502, PMCID: PMC4619847, DOI: 10.1155/2015/491502.Peer-Reviewed Original ResearchConceptsMachine learningF-measureAvailable big dataSupport vector machineBig dataVector machineClassification experimentsAccurate classificationCancer classificationGene function informationMachineSomatic mutation informationClassificationMutation informationFunction informationLearningGene symbolsInformationGene featuresGreat opportunityPerformanceSomatic mutation dataMutation dataAccuracyPrediction