Hua Xu, PhD
Cards
Appointments
Additional Titles
Vice Chair for Research and Development, Department of Biomedical Informatics and Data Science
Associate Dean for Biomedical Informatics, Yale School of Medicine
Contact Info
Biomedical Informatics & Data Science
100 College St
New Haven, Connecticut 06510
United States
Appointments
Additional Titles
Vice Chair for Research and Development, Department of Biomedical Informatics and Data Science
Associate Dean for Biomedical Informatics, Yale School of Medicine
Contact Info
Biomedical Informatics & Data Science
100 College St
New Haven, Connecticut 06510
United States
Appointments
Additional Titles
Vice Chair for Research and Development, Department of Biomedical Informatics and Data Science
Associate Dean for Biomedical Informatics, Yale School of Medicine
Contact Info
Biomedical Informatics & Data Science
100 College St
New Haven, Connecticut 06510
United States
About
Titles
Robert T. McCluskey Professor of Biomedical Informatics and Data Science
Vice Chair for Research and Development, Department of Biomedical Informatics and Data Science; Associate Dean for Biomedical Informatics, Yale School of Medicine
Biography
Dr. Hua Xu is a well-known researcher in clinical natural language processing (NLP). He has developed novel algorithms for important clinical NLP tasks such as entity recognition and relation extraction, which have been top ranked in over a dozen of international biomedical NLP challenges. His lab has developed CLAMP, a comprehensive clinical NLP toolkit that has been successfully commercialized and used by hundreds of healthcare organizations. Moreover, he has led multiple national/international initiatives (e.g., Chair of the NLP working group at Observational Health Data Sciences and Informatics - OHDSI program) to apply developed NLP technologies to diverse clinical and translational studies, thus greatly accelerating clinical evidence generation using electronic health records data. Recently, he also utilizes NLP to harmonize metadata of biomedical digital objects (e.g., indexing millions of biomedical datasets to make them findable), with the goal to promote FAIR principles in biomedicine. Currently Dr. Xu's lab is actively working on developing large language models (LLMs) for diverse biomedical applications. See more information about Dr. Xu's lab here.
Appointments
Biomedical Informatics & Data Science
ProfessorPrimary
Other Departments & Organizations
Education & Training
- PhD
- Columbia University, Biomedical Informatics
- MS
- New Jersey Institute of Technology, Computer Science
- BS
- Nanjing University, Biochemistry
Research
Overview
Medical Research Interests
ORCID
0000-0002-5274-4672- View Lab Website
Clinical NLP Lab
Research at a Glance
Yale Co-Authors
Publications Timeline
Research Interests
Lucila Ohno-Machado, MD, MBA, PhD
Vipina K. Keloth, PhD
Qingyu Chen, PhD
Kalpana Raja, PhD, MRSB, CSci
Tsung-Ting Kuo, PhD
Huan He, PhD
Natural Language Processing
Publications
Featured Publications
Medical foundation large language models for comprehensive text analysis and beyond
Xie Q, Chen Q, Chen A, Peng C, Hu Y, Lin F, Peng X, Huang J, Zhang J, Keloth V, Zhou X, Qian L, He H, Shung D, Ohno-Machado L, Wu Y, Xu H, Bian J. Medical foundation large language models for comprehensive text analysis and beyond. Npj Digital Medicine 2025, 8: 141. PMID: 40044845, PMCID: PMC11882967, DOI: 10.1038/s41746-025-01533-1.Peer-Reviewed Original ResearchCitationsAltmetricConceptsText analysis tasksAnalysis tasksLanguage modelDomain-specific knowledgeZero-ShotHuman evaluationSupervised settingTask-specific instructionsClinical data sourcesSpecialized medical knowledgeChatGPTText analysisPretrainingTaskData sourcesMedical applicationsMedical knowledgeEnhanced performanceTextPerformanceImproving large language models for clinical named entity recognition via prompt engineering
Hu Y, Chen Q, Du J, Peng X, Keloth V, Zuo X, Zhou Y, Li Z, Jiang X, Lu Z, Roberts K, Xu H. Improving large language models for clinical named entity recognition via prompt engineering. Journal Of The American Medical Informatics Association 2024, 31: 1812-1820. PMID: 38281112, PMCID: PMC11339492, DOI: 10.1093/jamia/ocad259.Peer-Reviewed Original ResearchCitationsConceptsClinical NER tasksNER taskTask-specific promptsEntity recognitionLanguage modelTraining samplesState-of-the-art modelsFew-shot learningState-of-the-artMinimal training dataTask-specific knowledgeF1-socreAnnotated samplesConcept extractionModel performanceAnnotated datasetsTraining dataF1 scoreTask descriptionFormat specificationsComplex clinical dataOptimal performanceTaskEvaluation schemaGPT modelBiomedRAG: A retrieval augmented large language model for biomedicine
Li M, Kilicoglu H, Xu H, Zhang R. BiomedRAG: A retrieval augmented large language model for biomedicine. Journal Of Biomedical Informatics 2025, 162: 104769. PMID: 39814274, PMCID: PMC11837810, DOI: 10.1016/j.jbi.2024.104769.Peer-Reviewed Original ResearchCitationsAltmetricMeSH Keywords and Concepts
2025
Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media
Li Y, Viswaroopan D, He W, Li J, Zuo X, Xu H, Tao C. Improving entity recognition using ensembles of deep learning and fine-tuned large language models: A case study on adverse event extraction from VAERS and social media. Journal Of Biomedical Informatics 2025, 163: 104789. PMID: 39923968, DOI: 10.1016/j.jbi.2025.104789.Peer-Reviewed Original ResearchCitationsMeSH Keywords and ConceptsConceptsTraditional deep learning modelsDeep learning modelsRecurrent neural networkLearning modelsEntity recognitionLanguage modelF1 scoreEnsemble of deep learningAdvances of natural language processingEffectiveness of ensemble methodsMicro-averaged F1Bidirectional Encoder RepresentationsExtensive labeled dataNatural language processingFine-tuned modelsBiomedical text miningFeature representationEncoder RepresentationsEvent extractionEntity typesText dataDeep learningSequential dataGPT-2Neural network
2024
SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials
Lee K, Paek H, Huang L, Hilton C, Datta S, Higashi J, Ofoegbu N, Wang J, Rubinstein S, Cowan A, Kwok M, Warner J, Xu H, Wang X. SEETrials: Leveraging large language models for safety and efficacy extraction in oncology clinical trials. Informatics In Medicine Unlocked 2024, 50: 101589. PMID: 39493413, PMCID: PMC11530223, DOI: 10.1016/j.imu.2024.101589.Peer-Reviewed Original ResearchCitationsAltmetricConceptsAntibody-drug conjugatesOverall response rateMultiple myelomaF1 scoreCAR-TComplete responseBispecific antibodiesComparative performance analysisClinical trial studyClinical trial outcomesLanguage modelAccurate data extractionTherapy subgroupFine granularityOncology clinical trialsAdverse eventsClinical decision-makingPerformance analysisClinical trialsInnovative therapiesDiverse therapiesClinical trial abstractsCancer domainData elementsTherapyRelation 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, 31: 2622-2631. PMID: 39208311, PMCID: PMC11491641, DOI: 10.1093/jamia/ocae233.Peer-Reviewed Original ResearchCitationsAltmetricConceptsAcupuncture 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, 157: 104705. PMID: 39134233, PMCID: PMC12235752, DOI: 10.1016/j.jbi.2024.104705.Peer-Reviewed Original ResearchConceptsAlzheimer'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, PMCID: PMC12235754, DOI: 10.1016/j.jbi.2024.104690.Peer-Reviewed Original ResearchConceptsElectronic 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 ResearchCitationsAltmetricConceptsElectronic 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, PMCID: PMC12032536, DOI: 10.1200/cci.23.00166.Peer-Reviewed Original ResearchAltmetricMeSH Keywords and ConceptsConceptsNatural 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 information
News
News
- September 16, 2025Source: NIH
Yale Team Recognized in NIH $1 Million Data Sharing Challenge
- July 01, 2025
Hua Xu, PhD, Receives NIH Supplement to Advance Mental Health Research
- May 14, 2025Source: Yale Medicine Magazine
Chatbot Revolution: From Me-LLaMA to GutGPT, YSM researchers leverage LLMs
- April 25, 2025
Yale BIDS Enhances Research with Comprehensive Data and Service Through YBIC
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Biomedical Informatics & Data Science
100 College St
New Haven, Connecticut 06510
United States
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