Hua Xu, PhD
he/him/his
Robert T. McCluskey Professor of Biomedical Informatics and Data ScienceCards
About
Research
Publications
2024
Relation extraction using large language models: a case study on acupuncture point locations
Li Y, Peng X, Li J, Zuo X, Peng S, Pei D, Tao C, Xu H, Hong N. Relation extraction using large language models: a case study on acupuncture point locations. Journal Of The American Medical Informatics Association 2024, ocae233. PMID: 39208311, DOI: 10.1093/jamia/ocae233.Peer-Reviewed Original ResearchAcupuncture point locationsAcupoint locationLocation of acupointsClinical decision supportAcupuncture knowledgeAcupuncture trainingAcupuncture therapyAcupunctureAcupointsComplementary medicineEducational moduleWestern Pacific RegionInformatics applicationsDecision supportScoresGenerative Pre-trained TransformerWHO standardsF1 scoreLanguage modelPacific regionWHODomain-specific fine-tuningTrainingStudyMicro-averaged F1 scoreBalancing the efforts of chart review and gains in PRS prediction accuracy: An empirical study
Lei Y, Christian Naj A, Xu H, Li R, Chen Y. Balancing the efforts of chart review and gains in PRS prediction accuracy: An empirical study. Journal Of Biomedical Informatics 2024, 104705. PMID: 39134233, DOI: 10.1016/j.jbi.2024.104705.Peer-Reviewed Original ResearchAlzheimer's Disease Genetics ConsortiumChart reviewPRS modelCase-control datasetGenetic association analysisGenetics ConsortiumPhenotype misclassificationSimulated phenotypesPhenotypic dataAssociation analysisEstimation of associated parametersBias reduction methodMedian thresholdPhenotypeMisclassification rateOriginal phenotypeDiverse arrayChartsMisclassificationGenotypesReviewEffects of biasBiasPrediction modelPRSLeveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data
Lu Y, Tong J, Chubak J, Lumley T, Hubbard R, Xu H, Chen Y. Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data. Journal Of Biomedical Informatics 2024, 157: 104690. PMID: 39004110, DOI: 10.1016/j.jbi.2024.104690.Peer-Reviewed Original ResearchElectronic health recordsElectronic health record dataKaiser Permanente WashingtonEHR-derived phenotypesAssociation studiesHealth recordsColon cancer recurrencePhenotyping errorsComputable phenotypeRisk factorsCancer recurrenceMultiple phenotypesReduce biasImprove estimation accuracySimulation studyBias reductionKaiserReduction of biasBiasEstimation accuracyAssociationStudyOutcomesRiskEstimation efficiencyDevelop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data
He X, Wei R, Huang Y, Chen Z, Lyu T, Bost S, Tong J, Li L, Zhou Y, Li Z, Guo J, Tang H, Wang F, DeKosky S, Xu H, Chen Y, Zhang R, Xu J, Guo Y, Wu Y, Bian J. Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data. Alzheimer's & Dementia Diagnosis Assessment & Disease Monitoring 2024, 16: e12613. PMID: 38966622, PMCID: PMC11220631, DOI: 10.1002/dad2.12613.Peer-Reviewed Original ResearchElectronic health record dataElectronic health recordsComputable phenotypeHealth record dataManual chart reviewHealth recordsAlzheimer's diseaseDiagnosis codesRecord dataChart reviewUTHealthAlzheimer's disease patientsUniversity of MinnesotaAD diagnosisAD identificationDisease patientsPatientsAlzheimerAD patientsDemographicsDiagnosisDiseaseCodeDataUniversityExtracting 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 informationNLP Applications—Other Biomedical Texts
Roberts K, Xu H, Demner Fushman D. NLP Applications—Other Biomedical Texts. Cognitive Informatics In Biomedicine And Healthcare 2024, 429-444. DOI: 10.1007/978-3-031-55865-8_15.Peer-Reviewed Original ResearchIntroduction to Natural Language Processing of Clinical Text
Demner Fushman D, Xu H. Introduction to Natural Language Processing of Clinical Text. Cognitive Informatics In Biomedicine And Healthcare 2024, 3-11. DOI: 10.1007/978-3-031-55865-8_1.Peer-Reviewed Original ResearchNatural language processingLanguage processingComplex language processingBiomedical natural language processingClinical natural language processingLanguage generation tasksClinical language processingBiomedical language processingLanguage modelClinical textGeneration taskMachine learningDelivery of informationClinical languageLanguageMedical 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 terminologyOntologyTaskLearningDevelopment of Clinical NLP Systems
Xu H, Demner Fushman D. Development of Clinical NLP Systems. Cognitive Informatics In Biomedicine And Healthcare 2024, 301-324. DOI: 10.1007/978-3-031-55865-8_11.Peer-Reviewed Original ResearchKamino: A Scalable Architecture to Support Medical AI Research Using Large Real World Data
Lin F, Young P, He H, Huang J, Gagne R, Rice D, Price N, Byron W, Hu Y, Felker D, Button W, Meeker D, Hsiao A, Xu H, Torre C, Schulz W. Kamino: A Scalable Architecture to Support Medical AI Research Using Large Real World Data. 2024, 00: 500-504. DOI: 10.1109/ichi61247.2024.00072.Peer-Reviewed Original ResearchElectronic health recordsAI researchNatural language processing tasksElectronic health record dataLanguage processing tasksComputing resource managementLarge-scale data retrievalMedical AI researchLeveraging electronic health recordsStandard data modelKubernetes orchestratorScalable architectureProcessing tasksResource allocation systemsSecurity considerationsAccess managementData retrievalData modelArchitectural solutionsOMOP CDMReal World DataWorld DataHealth recordsOMOPData
News
News
- September 10, 2024
NIH Recognizes Yale’s Expertise in the Genetics of Rare Diseases
- August 29, 2024
Yale Researchers Awarded $20.6M Grant for Wide-Ranging Study of Mental Illness
- July 06, 2024
MarketScan Data Now Available for Yale Researchers
- June 17, 2024
Hot off the Press: Natural Language Processing in Biomedicine