2020
The Data Tags Suite (DATS) model for discovering data access and use requirements
Alter G, Gonzalez-Beltran A, Ohno-Machado L, Rocca-Serra P. The Data Tags Suite (DATS) model for discovering data access and use requirements. GigaScience 2020, 9: giz165. PMID: 32031623, PMCID: PMC7006671, DOI: 10.1093/gigascience/giz165.Peer-Reviewed Original ResearchConceptsData accessData discovery toolsPrivacy of subjectsData use agreementsConfidential dataMetadata itemsData reuseMetadata schemaAutomated systemDiscovery toolTechnical systemsStandard wayUse agreementsAccessPrivacyMetadataSchemaUse requirementsReuseResearchersResearch dataSystemRequirementsInformationData
2018
A Scalable Privacy-preserving Data Generation Methodology for Exploratory Analysis.
Vaidya J, Shafiq B, Asani M, Adam N, Jiang X, Ohno-Machado L. A Scalable Privacy-preserving Data Generation Methodology for Exploratory Analysis. AMIA Annual Symposium Proceedings 2018, 2017: 1695-1704. PMID: 29854240, PMCID: PMC5977652.Peer-Reviewed Original ResearchConceptsPrivacy-preserving approachData management systemBig dataBiomedical datasetsClassification taskBiomedical dataContext of regressionManagement systemSynthetic dataGeneration methodologyEssential problemResearch tasksAdditional datasetsDatasetTaskSignificant effortsDirect accessFirstorder approximationDataParticular typeAccessPrecision medicinePerfectly Secure and Efficient Two-Party Electronic-Health-Record Linkage
Chen F, Jiang X, Wang S, Schilling L, Meeker D, Ong T, Matheny M, Doctor J, Ohno-Machado L, Vaidya J. Perfectly Secure and Efficient Two-Party Electronic-Health-Record Linkage. IEEE Internet Computing 2018, 22: 32-41. PMID: 29867290, PMCID: PMC5983039, DOI: 10.1109/mic.2018.112102542.Peer-Reviewed Original ResearchPrivacy-preserving record linkagePrivacy/security concernsRecord linkage solutionsApproximate matching mechanismsPatient health dataSecurity concernsMatching mechanismLinkage solutionsHealth recordsHealth dataRecord linkageSecureSharingFirst stepPersonalized careEfficientPortable methodHealth record linkageAccessPrecision medicineData
2015
Comparison of consumers’ views on electronic data sharing for healthcare and research
Kim K, Joseph J, Ohno-Machado L. Comparison of consumers’ views on electronic data sharing for healthcare and research. Journal Of The American Medical Informatics Association 2015, 22: 821-830. PMID: 25829461, PMCID: PMC5009901, DOI: 10.1093/jamia/ocv014.Peer-Reviewed Original ResearchConceptsElectronic data sharingData sharingHealth information exchangeData networksHealth informationTechnology infrastructureInformation exchangePrivacyHealth Insurance PortabilitySharingAccountability ActUse of dataInsurance PortabilitySecurityNetworkInformationHealthcareIndividual controlHealthcare deliveryMere relianceDepth studyPortabilityAccessInfrastructureComparison of consumers
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