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 algorithm
2023
Mining 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