2024
Detection of Gastrointestinal Bleeding with Large Language Models to Aid Quality Improvement and Appropriate Reimbursement
Zheng N, Keloth V, You K, Kats D, Li D, Deshpande O, Sachar H, Xu H, Laine L, Shung D. Detection of Gastrointestinal Bleeding with Large Language Models to Aid Quality Improvement and Appropriate Reimbursement. Gastroenterology 2024 PMID: 39304088, DOI: 10.1053/j.gastro.2024.09.014.Peer-Reviewed Original ResearchElectronic health recordsOvert gastrointestinal bleedingGastrointestinal bleedingRecurrent bleedingMachine learning modelsHealth recordsClinically relevant applicationsNursing notesLanguage modelAcute gastrointestinal bleedingQuality improvementLearning modelsDetection of gastrointestinal bleedingReimbursementIdentification of clinical conditionsSeparate hospitalsQuality measuresHospitalBleedingClinical conditionsPatient managementEarly identificationPatientsReimbursement codesCoding algorithmA Study of Biomedical Relation Extraction Using GPT Models.
Zhang J, Wibert M, Zhou H, Peng X, Chen Q, Keloth V, Hu Y, Zhang R, Xu H, Raja K. A Study of Biomedical Relation Extraction Using GPT Models. AMIA Joint Summits On Translational Science Proceedings 2024, 2024: 391-400. PMID: 38827097, PMCID: PMC11141827.Peer-Reviewed Original Research543 IDENTIFYING OVERT SIGNS OF ACUTE GASTROINTESTINAL BLEEDING IN THE ELECTRONIC HEALTH RECORD WITH LARGE LANGUAGE MODELS
Zheng N, Keloth V, You K, Li D, Xu H, Laine L, Shung D. 543 IDENTIFYING OVERT SIGNS OF ACUTE GASTROINTESTINAL BLEEDING IN THE ELECTRONIC HEALTH RECORD WITH LARGE LANGUAGE MODELS. Gastroenterology 2024, 166: s-124-s-125. DOI: 10.1016/s0016-5085(24)00776-5.Peer-Reviewed Original Research1244 AUTOMATED IDENTIFICATION OF RECURRENT GASTROINTESTINAL BLEEDING USING ELECTRONIC HEALTH RECORDS AND LARGE LANGUAGE MODELS
Zheng N, Keloth V, You K, Li D, Xu H, Laine L, Shung D. 1244 AUTOMATED IDENTIFICATION OF RECURRENT GASTROINTESTINAL BLEEDING USING ELECTRONIC HEALTH RECORDS AND LARGE LANGUAGE MODELS. Gastroenterology 2024, 166: s-292. DOI: 10.1016/s0016-5085(24)01152-1.Peer-Reviewed Original ResearchEnsemble pretrained language models to extract biomedical knowledge from literature
Li Z, Wei Q, Huang L, Li J, Hu Y, Chuang Y, He J, Das A, Keloth V, Yang Y, Diala C, Roberts K, Tao C, Jiang X, Zheng W, Xu H. Ensemble pretrained language models to extract biomedical knowledge from literature. Journal Of The American Medical Informatics Association 2024, 31: 1904-1911. PMID: 38520725, PMCID: PMC11339500, DOI: 10.1093/jamia/ocae061.Peer-Reviewed Original ResearchNatural language processingNatural language processing systemsLanguage modelExpansion of biomedical literatureZero-shot settingManually annotated corpusKnowledge graph developmentTask-specific modelsDomain-specific modelsZero-ShotEntity recognitionBillion parametersEnsemble learningLocation informationKnowledge basesBiomedical entitiesLanguage processingFree textGraph developmentBiomedical conceptsAutomated techniqueBiomedical literatureDetection methodPredictive performanceBiomedical knowledgeAdvancing entity recognition in biomedicine via instruction tuning of large language models
Keloth V, Hu Y, Xie Q, Peng X, Wang Y, Zheng A, Selek M, Raja K, Wei C, Jin Q, Lu Z, Chen Q, Xu H. Advancing entity recognition in biomedicine via instruction tuning of large language models. Bioinformatics 2024, 40: btae163. PMID: 38514400, PMCID: PMC11001490, DOI: 10.1093/bioinformatics/btae163.Peer-Reviewed Original ResearchNamed Entity RecognitionSequence labeling taskNatural language processingBiomedical NER datasetsLanguage modelNER datasetsEntity recognitionLabeling taskText generationField of natural language processingBiomedical NERFew-shot learning capabilityReasoning tasksMulti-domain scenariosDomain-specific modelsEnd-to-endMinimal fine-tuningSOTA performanceF1 scoreHealthcare applicationsBiomedical entitiesBiomedical domainLanguage processingMulti-taskingPubMedBERT modelFedFSA: Hybrid and federated framework for functional status ascertainment across institutions
Fu S, Jia H, Vassilaki M, Keloth V, Dang Y, Zhou Y, Garg M, Petersen R, St Sauver J, Moon S, Wang L, Wen A, Li F, Xu H, Tao C, Fan J, Liu H, Sohn S. FedFSA: Hybrid and federated framework for functional status ascertainment across institutions. Journal Of Biomedical Informatics 2024, 152: 104623. PMID: 38458578, PMCID: PMC11005095, DOI: 10.1016/j.jbi.2024.104623.Peer-Reviewed Original ResearchNatural language processingElectronic health recordsStatus informationInformation extractionFunctional status informationRule-based information extractionFederated learning frameworkPrivate local dataNatural language processing frameworkHealthcare sitesPatient's functional statusMultiple healthcare institutionsFederated learningPyTorch libraryConcept normalizationBERT modelLearning frameworkCollaborative development effortsCorpus annotationLanguage processingHealthcare institutionsFunctional statusPredictor of health outcomesActivities of daily livingNatural language processing performanceImproving 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 ResearchClinical 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 modelIntegrating Commercial and Social Determinants of Health: A Unified Ontology for Non-Clinical Determinants of Health.
Kollapally N, Keloth V, Xu J, Geller J. Integrating Commercial and Social Determinants of Health: A Unified Ontology for Non-Clinical Determinants of Health. AMIA Annual Symposium Proceedings 2024, 2023: 446-455. PMID: 38222328, PMCID: PMC10785916.Peer-Reviewed Original ResearchConceptsDeterminants of healthSocial determinants of healthImpact of social determinants of healthCommercial determinants of healthNon-clinical determinantsFactors affecting healthSocial determinantsHealth outcomesSDoHNonclinical determinantsHealthWell-beingPeople's healthNon-clinicalCDOHPubMed articlesPubMedSystematic approachOutcomesServicesPeopleSkimming of Electronic Health Records Highlighted by an Interface Terminology Curated with Machine Learning Mining
Koohi H. Dehkordi M, Kollapally N, Perl Y, Geller J, Deek F, Liu H, Keloth V, Elhanan G, Einstein A. Skimming of Electronic Health Records Highlighted by an Interface Terminology Curated with Machine Learning Mining. 2024, 498-505. DOI: 10.5220/0012391600003657.Peer-Reviewed Original Research
2023
Using annotation for computerized support for fast skimming of cardiology electronic health record notes
Dehkordi M, Einstein A, Zhou S, Elhanan G, Perl Y, Keloth V, Geller J, Liu H. Using annotation for computerized support for fast skimming of cardiology electronic health record notes. 2023, 00: 4043-4050. DOI: 10.1109/bibm58861.2023.10385289.Peer-Reviewed Original ResearchElectronic health recordsElectronic health record notesNamed Entity RecognitionTraining dataMining conceptsInterface terminologyMachine learningSNOMED CTEntity recognitionHealth recordsHealthcare professionalsTransfer learningPatient careCurrent healthcareMining techniquesMining phrasesArt techniquesRecord notesSNOMED conceptsMedical specialtiesComputer-SupportedMedical professionalsReference terminologyCritical informationAnnotationTowards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach
Hu Y, Keloth V, Raja K, Chen Y, Xu H. Towards precise PICO extraction from abstracts of randomized controlled trials using a section-specific learning approach. Bioinformatics 2023, 39: btad542. PMID: 37669123, PMCID: PMC10500081, DOI: 10.1093/bioinformatics/btad542.Peer-Reviewed Original ResearchNatural language processingMicro-F1 scoreCOVID-19 datasetNLP pipelineF1 scoreEntity recognition modelAD datasetPICO elementsSentence classificationNER modelRecognition modelLanguage processingLearning approachLearning modelEnd evaluationSupplementary dataDatasetPipelineExtractionInformationRCT abstractsAnnotationSentencesBioinformaticsComplexitySystematic 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 ResearchRepresenting and utilizing clinical textual data for real world studies: An OHDSI approach
Keloth V, Banda J, Gurley M, Heider P, Kennedy G, Liu H, Liu F, Miller T, Natarajan K, V Patterson O, Peng Y, Raja K, Reeves R, Rouhizadeh M, Shi J, Wang X, Wang Y, Wei W, Williams A, Zhang R, Belenkaya R, Reich C, Blacketer C, Ryan P, Hripcsak G, Elhadad N, Xu H. Representing and utilizing clinical textual data for real world studies: An OHDSI approach. Journal Of Biomedical Informatics 2023, 142: 104343. PMID: 36935011, PMCID: PMC10428170, DOI: 10.1016/j.jbi.2023.104343.Peer-Reviewed Original ResearchConceptsNatural language processingCommon data modelTextual dataNLP solutionObservational Health Data SciencesOMOP Common Data ModelSpecific use casesObservational Medical Outcomes Partnership Common Data ModelHealth Data SciencesRepresentation of informationUse casesElectronic health recordsReal-world evidence generationData scienceClinical textData modelClinical notesLanguage processingHealth recordsLoad dataClinical documentationCurrent applicationsInformationWorkflowEvidence generationMining of EHR for interface terminology concepts for annotating EHRs of COVID patients
Keloth V, Zhou S, Lindemann L, Zheng L, Elhanan G, Einstein A, Geller J, Perl Y. Mining of EHR for interface terminology concepts for annotating EHRs of COVID patients. BMC Medical Informatics And Decision Making 2023, 23: 40. PMID: 36829139, PMCID: PMC9951157, DOI: 10.1186/s12911-023-02136-0.Peer-Reviewed Original ResearchConceptsElectronic health recordsCoronavirus Infectious Disease OntologyGranular conceptsTextual dataInterface terminologyVolume of textual dataSNOMED CTLack of annotationsMining of electronic health recordsMachine learning modelsInfectious Disease OntologyTraining dataAutomatic annotationAutomatic extractionLearning modelsMining approachHold-out datasetElectronic health record dataCOVID-19 terminologyHealth recordsAnnotationOntologyDisease OntologyDatasetSNOMED
2021
Visual comprehension and orientation into the COVID-19 CIDO ontology
Zheng L, Perl Y, He Y, Ochs C, Geller J, Liu H, Keloth V. Visual comprehension and orientation into the COVID-19 CIDO ontology. Journal Of Biomedical Informatics 2021, 120: 103861. PMID: 34224898, PMCID: PMC8252699, DOI: 10.1016/j.jbi.2021.103861.Peer-Reviewed Original ResearchConceptsPartial-area taxonomyCoronavirus Infectious Disease OntologyCompact viewMedium sized ontologiesNames of nodesVisual patternsInfectious Disease OntologySummarization networkOntologyUsersVisual comprehensionDisease OntologyVisualizationNodesTaxonomyLayoutSummarizationGranularityNetworkCIDOConceptViews
2020
Generating Training Data for Concept-Mining for an ‘Interface Terminology’ Annotating Cardiology EHRs
Keloth V, Zhou S, Einstein A, Elhanan G, Chen Y, Geller J, Perl Y. Generating Training Data for Concept-Mining for an ‘Interface Terminology’ Annotating Cardiology EHRs. 2020, 00: 1728-1735. DOI: 10.1109/bibm49941.2020.9313435.Peer-Reviewed Original ResearchExtending import detection algorithms for concept import from two to three biomedical terminologies
Keloth V, Geller J, Chen Y, Xu J. Extending import detection algorithms for concept import from two to three biomedical terminologies. BMC Medical Informatics And Decision Making 2020, 20: 272. PMID: 33319702, PMCID: PMC7737255, DOI: 10.1186/s12911-020-01290-z.Peer-Reviewed Original ResearchConceptsTarget terminologySource terminologiesGeneration of candidatesDomain expertsCandidate conceptsIs-aDetection algorithmHuman effortManual inspectionAlgorithmic discoveryConcept importanceAlgorithmic generationAlgorithmic detectionLadder patternConfiguration of conceptsUMLS terminologyImport of conceptsOutlier concepts auditing methodology for a large family of biomedical ontologies
Zheng L, Min H, Chen Y, Keloth V, Geller J, Perl Y, Hripcsak G. Outlier concepts auditing methodology for a large family of biomedical ontologies. BMC Medical Informatics And Decision Making 2020, 20: 296. PMID: 33319713, PMCID: PMC7737254, DOI: 10.1186/s12911-020-01311-x.Peer-Reviewed Original ResearchConceptsFamily of ontologiesSummarization networkMeta-ontologyQA techniquesPartial-area taxonomySets of conceptsSpecimen hierarchyScalabilityQA frameworkOntology hierarchyOntologySummarizationNetworkBioPortal ontologiesAudit methodologyQuality assuranceHierarchyNCBOBig pictureCollection of familiesGene hierarchySNOMEDMining Concepts for a COVID Interface Terminology for Annotation of EHRs
Keloth V, Zhou S, Lindemann L, Elhanan G, Einstein A, Geller J, Perl Y. Mining Concepts for a COVID Interface Terminology for Annotation of EHRs. 2020, 00: 3753-3760. DOI: 10.1109/bigdata50022.2020.9377981.Peer-Reviewed Original ResearchInterface terminologyDeluge of medical dataAnnotated clinical notesMachine learning techniquesConcept miningTraining dataClinical textHuge volumesLearning techniquesMining conceptsGranular conceptsMedical dataEHRInitial versionIncomplete dataAnnotationMiningCOVID-19 patientsHealthcare servicesConcatenationHealthcare deliveryDelugeOntologyMachineConcept