2023
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 generation
2020
Learning from local to global: An efficient distributed algorithm for modeling time-to-event data
Duan R, Luo C, Schuemie M, Tong J, Liang C, Chang H, Boland M, Bian J, Xu H, Holmes J, Forrest C, Morton S, Berlin J, Moore J, Mahoney K, Chen Y. Learning from local to global: An efficient distributed algorithm for modeling time-to-event data. Journal Of The American Medical Informatics Association 2020, 27: 1028-1036. PMID: 32626900, PMCID: PMC7647322, DOI: 10.1093/jamia/ocaa044.Peer-Reviewed Original Research
2018
Association of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin
Vashisht R, Jung K, Schuler A, Banda J, Park R, Jin S, Li L, Dudley J, Johnson K, Shervey M, Xu H, Wu Y, Natrajan K, Hripcsak G, Jin P, Van Zandt M, Reckard A, Reich C, Weaver J, Schuemie M, Ryan P, Callahan A, Shah N. Association of Hemoglobin A1c Levels With Use of Sulfonylureas, Dipeptidyl Peptidase 4 Inhibitors, and Thiazolidinediones in Patients With Type 2 Diabetes Treated With Metformin. JAMA Network Open 2018, 1: e181755. PMID: 30646124, PMCID: PMC6324274, DOI: 10.1001/jamanetworkopen.2018.1755.Peer-Reviewed Original ResearchConceptsDPP-4 inhibitorsDipeptidyl peptidase-4 inhibitorsFirst-line therapyPeptidase-4 inhibitorsSecond-line drugsType 2 diabetesMyocardial infarctionEye disordersKidney disordersDrug classesSecond-line treatment choiceTotal hemoglobinObservational Health Data SciencesSecond-line treatment optionNew-user cohort studyEffectiveness of sulfonylureasSecond-line treatmentHemoglobin A1c levelsUse of sulfonylureasHealth Data SciencesLarge international studyElectronic medical recordsRoutine medical practiceInsurance claims dataCohort study
2017
Risk of angioedema associated with levetiracetam compared with phenytoin: Findings of the observational health data sciences and informatics research network
Duke J, Ryan P, Suchard M, Hripcsak G, Jin P, Reich C, Schwalm M, Khoma Y, Wu Y, Xu H, Shah N, Banda J, Schuemie M. Risk of angioedema associated with levetiracetam compared with phenytoin: Findings of the observational health data sciences and informatics research network. Epilepsia 2017, 58: e101-e106. PMID: 28681416, PMCID: PMC6632067, DOI: 10.1111/epi.13828.Peer-Reviewed Original ResearchConceptsAngioedema riskAngioedema eventsHazard ratioObservational Health Data SciencesNew-user cohort studySummary hazard ratioRisk of angioedemaHealth Data SciencesAdverse event reportsPhenytoin usersResearch NetworkPhenytoin groupCohort studyTreat analysisAntiepileptic drugsComparator groupSeizure patientsLower riskLevetiracetamAngioedemaFurther studiesEvent reportsSignificant increaseRiskPhenytoin