2013
Detecting inappropriate access to electronic health records using collaborative filtering
Menon A, Jiang X, Kim J, Vaidya J, Ohno-Machado L. Detecting inappropriate access to electronic health records using collaborative filtering. Machine Learning 2013, 95: 87-101. PMID: 24683293, PMCID: PMC3967851, DOI: 10.1007/s10994-013-5376-1.Peer-Reviewed Original ResearchElectronic health recordsCollaborative filteringInappropriate accessHealth recordsSuspicious accessPrivacy policiesAccess patternsMachine learningManual auditingSecurity expertsLatent featuresAccess dataRecord accessHistorical dataSecurityFilteringUnrestricted accessFuture violationsAccessAudit processSVMUsersDatasetLearningAuditing
2011
Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs.
Kim J, Grillo J, Boxwala A, Jiang X, Mandelbaum R, Patel B, Mikels D, Vinterbo S, Ohno-Machado L. Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs. AMIA Annual Symposium Proceedings 2011, 2011: 723-31. PMID: 22195129, PMCID: PMC3243249.Peer-Reviewed Original ResearchConceptsSuspicious accessAccess recordsRule-based techniquesMachine learning methodsConstruction of classifiersAnomaly detectionInformative instancesLearning methodsSymbolic clusteringClassifier performanceSignature detectionIndependent test setInappropriate accessTest setEHRFiltering methodIntegrated filtering strategyFiltering strategyClassifierFilteringNegative rateFalse negative rateAccessDetectionClustering