Representing 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