2023
A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data
Yan Z, Zachrison K, Schwamm L, Estrada J, Duan R. A privacy-preserving and computation-efficient federated algorithm for generalized linear mixed models to analyze correlated electronic health records data. PLOS ONE 2023, 18: e0280192. PMID: 36649349, PMCID: PMC9844867, DOI: 10.1371/journal.pone.0280192.Peer-Reviewed Original ResearchConceptsFederated algorithmPrivacy-preserving data integrationEHR dataElectronic health record dataComputation resource requirementsHealth record dataLongitudinal EHR dataPrivacy protectionData integrationResource requirementsMultiple healthcare facilitiesNumerical experimentsComputational efficiencyGeneralized linear mixed modelRecord dataCorrelated dataSite‐level heterogeneityAlgorithmNetworkSummary statisticsResearch NetworkLimited amountDatasetLinear mixed modelsGLMM
2017
Incorporating Stroke Severity Into Hospital Measures of 30-Day Mortality After Ischemic Stroke Hospitalization
Schwartz J, Wang Y, Qin L, Schwamm LH, Fonarow GC, Cormier N, Dorsey K, McNamara RL, Suter LG, Krumholz HM, Bernheim SM. Incorporating Stroke Severity Into Hospital Measures of 30-Day Mortality After Ischemic Stroke Hospitalization. Stroke 2017, 48: 3101-3107. PMID: 28954922, DOI: 10.1161/strokeaha.117.017960.Peer-Reviewed Original ResearchConceptsRisk-standardized mortality ratesElectronic health record dataHealth record dataStroke severityClaims dataMortality rateAmerican Heart Association/American Stroke AssociationHealth Stroke Scale scoreRisk variablesMedicaid ServicesRisk adjustmentMedian risk-standardized mortality rateGuidelines-Stroke registryLow-mortality hospitalsStroke Scale scoreAcute ischemic strokeAmerican Stroke AssociationOdds of mortalityMortality measuresRecord dataIschemic stroke hospitalizationsHigh-mortality hospitalsService claims dataRisk-adjustment variablesHospital admission