2018
Data science and artificial intelligence to improve clinical practice and research
Ohno-Machado L. Data science and artificial intelligence to improve clinical practice and research. Journal Of The American Medical Informatics Association 2018, 25: 1273-1273. PMID: 30312446, PMCID: PMC7646927, DOI: 10.1093/jamia/ocy136.Commentaries, Editorials and Letters
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 ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceHealth Services ResearchHumansInformation DisseminationMedical Informatics ComputingPrivacySoftwareSupport Vector MachineConceptsPrivate 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 dataServer
2011
Improving predictions in imbalanced data using Pairwise Expanded Logistic Regression.
Jiang X, El-Kareh R, Ohno-Machado L. Improving predictions in imbalanced data using Pairwise Expanded Logistic Regression. AMIA Annual Symposium Proceedings 2011, 2011: 625-34. PMID: 22195118, PMCID: PMC3243279.Peer-Reviewed Original ResearchArtificial IntelligenceDiseaseHumansLogistic ModelsMathematical ConceptsROC CurveSensitivity and SpecificityAnomaly 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 ResearchConceptsSuspicious 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
2009
Outstanding Submissions to the AMIA Annual Symposium Now Featured in JAMIA
Ohno-Machado L, Miller R. Outstanding Submissions to the AMIA Annual Symposium Now Featured in JAMIA. Journal Of The American Medical Informatics Association 2009, 16: 143-144. PMID: 19127607, PMCID: PMC2605591, DOI: 10.1197/jamia.m3021.Commentaries, Editorials and Letters
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 modelApplications
2006
PROGNOSIS IN CRITICAL CARE
Ohno-Machado L, Resnic F, Matheny M. PROGNOSIS IN CRITICAL CARE. Annual Review Of Biomedical Engineering 2006, 8: 567-599. PMID: 16834567, DOI: 10.1146/annurev.bioeng.8.061505.095842.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus Statements
2005
Small, fuzzy and interpretable gene expression based classifiers
Vinterbo S, Kim E, Ohno-Machado L. Small, fuzzy and interpretable gene expression based classifiers. Bioinformatics 2005, 21: 1964-1970. PMID: 15661797, DOI: 10.1093/bioinformatics/bti287.Peer-Reviewed Original ResearchCombining Classifiers Using Their Receiver Operating Characteristics and Maximum Likelihood Estimation
Haker S, Wells W, Warfield S, Talos I, Bhagwat J, Goldberg-Zimring D, Mian A, Ohno-Machado L, Zou K. Combining Classifiers Using Their Receiver Operating Characteristics and Maximum Likelihood Estimation. Lecture Notes In Computer Science 2005, 8: 506-514. PMID: 16685884, PMCID: PMC3681096, DOI: 10.1007/11566465_63.Peer-Reviewed Original Research
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 waveDataSetA greedy algorithm for supervised discretization
Butterworth R, Simovici D, Santos G, Ohno-Machado L. A greedy algorithm for supervised discretization. Journal Of Biomedical Informatics 2004, 37: 285-292. PMID: 15465481, DOI: 10.1016/j.jbi.2004.07.006.Peer-Reviewed Original ResearchResearch on machine learning issues in biomedical informatics modeling
Ohno-Machado L. Research on machine learning issues in biomedical informatics modeling. Journal Of Biomedical Informatics 2004, 37: 221-223. PMID: 15465475, DOI: 10.1016/j.jbi.2004.07.004.Commentaries, Editorials and LettersMultivariate 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
Building an asynchronous web-based tool for machine learning classification.
Weber G, Vinterbo S, Ohno-Machado L. Building an asynchronous web-based tool for machine learning classification. AMIA Annual Symposium Proceedings 2002, 869-73. PMID: 12463949, PMCID: PMC2244467.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceDNA, NeoplasmGene ExpressionGenetic MarkersHumansInternetLogistic ModelsNeoplasmsOligonucleotide Array Sequence AnalysisSoftwareConceptsClassification methodDownloadable software toolsWeb-based applicationSupervised learning methodsSupport vector machineNearest neighbor classifierWeb-based toolWeb-based analysis toolWeb applicationBioinformatics LaboratorySoftware toolsFree softwareLearning methodsSophisticated algorithmsVector machineNeighbor classifierLinear discriminant analysisAnalysis toolsDisease classificationClassification treesLimited budgetShort amountRemote locationsHigh-throughput gene expression dataGene expression data
2001
Effects of Case Removal in Prognostic Models
Ohno-Machado L, Vinterbo S. Effects of Case Removal in Prognostic Models. Methods Of Information In Medicine 2001, 40: 32-38. PMID: 11310157, DOI: 10.1055/s-0038-1634461.Peer-Reviewed Original Research
2000
Unsupervised learning from complex data: the matrix incision tree algorithm.
Kim J, Ohno-Machado L, Kohane I. Unsupervised learning from complex data: the matrix incision tree algorithm. Biocomputing 2000, 30-41. PMID: 11262950, DOI: 10.1142/9789814447362_0004.Peer-Reviewed Original ResearchConceptsHigh-dimensional spaceTree algorithmComplex high-dimensional spacesPredictive model buildingData setsLarge-scale gene expression dataLow-dimensional spaceKnowledge discoveryUnsupervised learningData structureComplex dataNovel methodMeaningful structuresMicroarray data setsDNA microarray data setsAlgorithmBuilding knowledge in a complex preterm birth problem domain.
Goodwin L, Maher S, Ohno-Machado L, Iannacchione M, Crockett P, Dreiseitl S, Vinterbo S, Hammond W. Building knowledge in a complex preterm birth problem domain. AMIA Annual Symposium Proceedings 2000, 305-9. PMID: 11079894, PMCID: PMC2243761.Peer-Reviewed Original Research
1999
Evaluating variable selection methods for diagnosis of myocardial infarction.
Dreiseitl S, Ohno-Machado L, Vinterbo S. Evaluating variable selection methods for diagnosis of myocardial infarction. AMIA Annual Symposium Proceedings 1999, 246-50. PMID: 10566358, PMCID: PMC2232647.Peer-Reviewed Original ResearchConceptsMachine-learning techniquesBayesian neural networksNeural networkMultilayer perceptronRough setsVariable selection methodsSelection methodInput variablesVariable selectionInfarction dataBackpropagationPerceptronMyocardial infarction dataDifferent subsetsAlgorithmNetworkMethodSetDifferent methods