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 ResearchLanguage model1244 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 modelImproving 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 model