2014
Privacy Preserving RBF Kernel Support Vector Machine
Li H, Xiong L, Ohno-Machado L, Jiang X. Privacy Preserving RBF Kernel Support Vector Machine. BioMed Research International 2014, 2014: 827371. PMID: 25013805, PMCID: PMC4071990, DOI: 10.1155/2014/827371.Peer-Reviewed Original ResearchConceptsPrivate dataPrivacy-preserving data disseminationKernel support vector machineRBF kernel support vector machinePublic dataSupport vector machineSupport vector machine modelVector machine modelData disseminationData sharingBiomedical dataPrivacy standardsVector machineRBF kernelPerformance metricsSVMMachine modelFull usePrivacyFinal outputSeparable caseAvailable informationMachineSharingMetrics
2012
A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning.
Que J, Jiang X, Ohno-Machado L. A collaborative framework for Distributed Privacy-Preserving Support Vector Machine learning. AMIA Annual Symposium Proceedings 2012, 2012: 1350-9. PMID: 23304414, PMCID: PMC3540462.Peer-Reviewed Original ResearchConceptsSupport vector machineVector machinePrivacy-preserving collaborative learningSensitive raw dataPrivacy-preserving mannerEfficient information exchangeDistributed PrivacyLocal repositoryPrivacy concernsCentralized repositoryCollaborative frameworkDecision supportMultiple participantsInformation exchangeRaw dataSVM modelIntermediary resultsMachineCollaborative learningPrivacyPopular toolRepositoryTraditional wayPatient dataServerPredicting accurate probabilities with a ranking loss.
Menon A, Jiang X, Vembu S, Elkan C, Ohno-Machado L. Predicting accurate probabilities with a ranking loss. Proceedings Of The ... International Conference On Machine Learning. 2012, 2012: 703-710. PMID: 25285328, PMCID: PMC4180410.Peer-Reviewed Original ResearchReal-world applicationsProbability distributionStatistical workhorseIsotonic regressionAccurate probabilityParticular classProbability predictionRegression performanceSemi-parametric techniquesRanking lossRich setBest rankingProbabilityExperimental resultsClassifierApplicationsMachineTechniqueClassSimple technique
2007
Effects of SVM parameter optimization on discrimination and calibration for post-procedural PCI mortality
Matheny M, Resnic F, Arora N, Ohno-Machado L. Effects of SVM parameter optimization on discrimination and calibration for post-procedural PCI mortality. Journal Of Biomedical Informatics 2007, 40: 688-697. PMID: 17600771, PMCID: PMC2170520, DOI: 10.1016/j.jbi.2007.05.008.Peer-Reviewed Original ResearchConceptsSupport vector machineRadial Basis Kernel Support Vector MachineKernel support vector machineCross-entropy errorSVM parameter optimizationUnseen test dataSVM kernel typesTraining dataVector machineEvolutionary algorithmGrid searchMean squared errorKernel typeMachineOptimization methodPrediction modelNumber of methodsParameter optimizationTest dataMedical applicationsOptimization parametersMortality prediction modelAlgorithmBest modelApplications10 Generation of knowledge for clinical decision support Statistical and machine learning techniques
Matheny M, Ohno-Machado L. 10 Generation of knowledge for clinical decision support Statistical and machine learning techniques. 2007, 227-248. DOI: 10.1016/b978-012369377-8/50011-8.ChaptersLearning techniquesMedical domainMachine learning techniquesAcceptance of computersClinical decision supportModeling methodAlternative modeling methodDecision supportBiomedical informaticsPopular examplePrediction modelGeneration of knowledgeTheoretical justificationRapid paceRegression techniquesTechnological advancesComputerInformaticsMachineBest modelTechniqueDomainClassificationModelKnowledge
2004
A primer on gene expression and microarrays for machine learning researchers
Kuo W, Kim E, Trimarchi J, Jenssen T, Vinterbo S, Ohno-Machado L. A primer on gene expression and microarrays for machine learning researchers. Journal Of Biomedical Informatics 2004, 37: 293-303. PMID: 15465482, DOI: 10.1016/j.jbi.2004.07.002.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsNew algorithmSupervised learning modelUCI machineLearning modelMicroarray data analysisAlgorithmic developmentsTypes of dataMachineData setsMain challengesGene expression dataMain motivationAlgorithmData analysisBiomedical experimentsLarge numberExpression dataMicroarray dataResearchersRepositoryWebMicroarray experimentsNew waveDataSetMultivariate selection of genetic markers in diagnostic classification
Weber G, Vinterbo S, Ohno-Machado L. Multivariate selection of genetic markers in diagnostic classification. Artificial Intelligence In Medicine 2004, 31: 155-167. PMID: 15219292, DOI: 10.1016/j.artmed.2004.01.011.Peer-Reviewed Original ResearchConceptsClassification performanceBetter classification performanceLogistic regression algorithmUser-friendly implementationDifferent data setsSophisticated algorithmsRegression algorithmAlgorithmNew algorithmParticular classificationUnivariate algorithmsData setsGene expression dataClassificationNumber of variablesGene selectionSetInternetExpression dataNew setViable choiceMachinePerformanceImplementationSelection
2002
Logistic regression and artificial neural network classification models: a methodology review
Dreiseitl S, Ohno-Machado L. Logistic regression and artificial neural network classification models: a methodology review. Journal Of Biomedical Informatics 2002, 35: 352-359. PMID: 12968784, DOI: 10.1016/s1532-0464(03)00034-0.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsMedical data classification tasksNeural network classification modelArtificial neural network (ANN) classification modelData classification tasksNetwork classification modelArtificial neural networkArtificial neural network modelNeural network modelClassification taskNeural networkClassification modelNetwork modelTechnical pointMachineAlgorithmNetworkTaskQuality criteriaModelMethodology reviewSample of papers
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
1998
Improving machine learning performance by removing redundant cases in medical data sets.
Ohno-Machado L, Fraser H, Ohrn A. Improving machine learning performance by removing redundant cases in medical data sets. AMIA Annual Symposium Proceedings 1998, 523-7. PMID: 9929274, PMCID: PMC2232167.Peer-Reviewed Original Research