2013
Comparison of four prediction models to discriminate benign from malignant vertebral compression fractures according to MRI feature analysis.
Thawait S, Kim J, Klufas R, Morrison W, Flanders A, Carrino J, Ohno-Machado L. Comparison of four prediction models to discriminate benign from malignant vertebral compression fractures according to MRI feature analysis. American Journal Of Roentgenology 2013, 200: 493-502. PMID: 23436836, DOI: 10.2214/ajr.11.7192.Peer-Reviewed Original ResearchAdultAgedAged, 80 and overAlgorithmsCohort StudiesComputer SimulationDiagnosis, DifferentialFemaleFractures, CompressionHumansImage EnhancementImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedModels, BiologicalNeoplasmsPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySpinal FracturesYoung Adult
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 ResearchMeSH KeywordsArtificial IntelligenceComputer SecurityElectronic Health RecordsHumansLogistic ModelsPrivacySensitivity and SpecificityConceptsSuspicious accessAccess recordsRule-based techniquesMachine learning methodsConstruction of classifiersAnomaly detectionInformative instancesLearning methodsSymbolic clusteringClassifier performanceSignature detectionIndependent test setInappropriate accessTest setEHRFiltering methodIntegrated filtering strategyFiltering strategyClassifierFilteringNegative rateFalse negative rateAccessDetectionClusteringUsing statistical and machine learning to help institutions detect suspicious access to electronic health records
Boxwala A, Kim J, Grillo J, Ohno-Machado L. Using statistical and machine learning to help institutions detect suspicious access to electronic health records. Journal Of The American Medical Informatics Association 2011, 18: 498-505. PMID: 21672912, PMCID: PMC3128412, DOI: 10.1136/amiajnl-2011-000217.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceComputer SecurityElectronic Health RecordsHumansLogistic ModelsManagement AuditPilot ProjectsSensitivity and SpecificitySoftware ValidationUnited StatesConceptsSuspicious accessMachine-learning methodsPrivacy officersMachine learning techniquesVector machine modelAccess logsElectronic health recordsBaseline methodsAccess dataCross-validation setGold standard setSVM modelWhole data setMachine modelBaseline modelOrganizational dataHealth recordsData setsSVM