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
2001
A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions
Dreiseitl S, Ohno-Machado L, Kittler H, Vinterbo S, Billhardt H, Binder M. A Comparison of Machine Learning Methods for the Diagnosis of Pigmented Skin Lesions. Journal Of Biomedical Informatics 2001, 34: 28-36. PMID: 11376540, DOI: 10.1006/jbin.2001.1004.Peer-Reviewed Original ResearchConceptsArtificial neural networkDichotomous problemNearest neighborsDifferent classification tasksSpecific classification problemMachine learning methodsMachine-learning methodsClassification taskClassification problemNeural networkLearning methodsDecision tressPigmented skin lesionsVector machineDecision treeTaskNeighborsSVMMachineNetworkBenchmarksCommon neviMethodExcellent results