2023
A hierarchical strategy to minimize privacy risk when linking “De-identified” data in biomedical research consortia
Ohno-Machado L, Jiang X, Kuo T, Tao S, Chen L, Ram P, Zhang G, Xu H. A hierarchical strategy to minimize privacy risk when linking “De-identified” data in biomedical research consortia. Journal Of Biomedical Informatics 2023, 139: 104322. PMID: 36806328, PMCID: PMC10975485, DOI: 10.1016/j.jbi.2023.104322.Peer-Reviewed Original ResearchConceptsPrivacy of individualsAppropriate privacy protectionData-driven modelsPrivacy protectionPrivacy risksData Coordination CenterData hubData repositoryHierarchical strategyPrivacyBiomedical discoveryData setsRecord linkageData Coordinating CenterRepositoryComplex strategiesCoordination centerTechnologyTechniqueDataPartiesSetHierarchy
2017
DATS, the data tag suite to enable discoverability of datasets
Sansone S, Gonzalez-Beltran A, Rocca-Serra P, Alter G, Grethe J, Xu H, Fore I, Lyle J, Gururaj A, Chen X, Kim H, Zong N, Li Y, Liu R, Ozyurt I, Ohno-Machado L. DATS, the data tag suite to enable discoverability of datasets. Scientific Data 2017, 4: 170059. PMID: 28585923, PMCID: PMC5460592, DOI: 10.1038/sdata.2017.59.Peer-Reviewed Original ResearchFinding useful data across multiple biomedical data repositories using DataMed
Ohno-Machado L, Sansone S, Alter G, Fore I, Grethe J, Xu H, Gonzalez-Beltran A, Rocca-Serra P, Gururaj A, Bell E, Soysal E, Zong N, Kim H. Finding useful data across multiple biomedical data repositories using DataMed. Nature Genetics 2017, 49: 816-819. PMID: 28546571, PMCID: PMC6460922, DOI: 10.1038/ng.3864.Peer-Reviewed Original ResearchConceptsBiomedical data repositoriesHealth big dataData setsKnowledge discoveryBig dataMultiple repositoriesSearch enginesData indexFAIR principlesDataMedData repositoryService providersKnowledge initiativesKnowledge expertsBiomedical research communityResearch communityRepositoryScience landscapeUseful dataInteroperabilityMetadataFindabilitySetEngineData
2012
Grid Binary LOgistic REgression (GLORE): building shared models without sharing data
Wu Y, Jiang X, Kim J, Ohno-Machado L. Grid Binary LOgistic REgression (GLORE): building shared models without sharing data. Journal Of The American Medical Informatics Association 2012, 19: 758-764. PMID: 22511014, PMCID: PMC3422844, DOI: 10.1136/amiajnl-2012-000862.Peer-Reviewed Original ResearchConceptsIntegrity of communicationCentralized data sourcesTraditional LR modelCentral repositoryComputational costData sourcesData setsSame formatPatient dataComputationGenomic dataRare patternRelevant dataLR modelPrediction valueSetRepositoryPartial elementsFormatClassificationCommunicationModelDataPatient setPerform
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
2003
Stochastic Algorithms for Gene Expression Analysis
Ohno-Machado L, Kuo W. Stochastic Algorithms for Gene Expression Analysis. Lecture Notes In Computer Science 2003, 2827: 39-49. DOI: 10.1007/978-3-540-39816-5_4.Peer-Reviewed Original Research
2002
Disambiguation Data: Extracting Information from Anonymized Sources
Dreiseitl S, Vinterbo S, Ohno-Machado L. Disambiguation Data: Extracting Information from Anonymized Sources. Journal Of The American Medical Informatics Association 2002, 9: s110-s114. PMCID: PMC419432, DOI: 10.1197/jamia.m1240.Peer-Reviewed Original Research
2001
Disambiguation data: extracting information from anonymized sources.
Dreiseitl S, Vinterbo S, Ohno-Machado L. Disambiguation data: extracting information from anonymized sources. AMIA Annual Symposium Proceedings 2001, 144-8. PMID: 11825171, PMCID: PMC2243291.Peer-Reviewed Original ResearchGeneration of dynamically configured check lists for intra-operative problems using a set of covering algorithms.
Sawa T, Ohno-Machado L. Generation of dynamically configured check lists for intra-operative problems using a set of covering algorithms. AMIA Annual Symposium Proceedings 2001, 593-7. PMID: 11837218, PMCID: PMC2243696.Peer-Reviewed Original Research
2000
A genetic algorithm approach to multi-disorder diagnosis
Vinterbo S, Ohno-Machado L. A genetic algorithm approach to multi-disorder diagnosis. Artificial Intelligence In Medicine 2000, 18: 117-132. PMID: 10648846, DOI: 10.1016/s0933-3657(99)00036-6.Peer-Reviewed Original ResearchBuilding 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 methodsA genetic algorithm to select variables in logistic regression: example in the domain of myocardial infarction.
Vinterbo S, Ohno-Machado L. A genetic algorithm to select variables in logistic regression: example in the domain of myocardial infarction. AMIA Annual Symposium Proceedings 1999, 984-8. PMID: 10566508, PMCID: PMC2232877.Peer-Reviewed Original ResearchConceptsGenetic algorithmNumber of variablesVariable selection methodsGenetic algorithm variable selection methodSelection methodData setsAlgorithmVariable selectionBest variable combinationModel's discriminatory performanceModel simplicityActual useValidation setExternal validation setSetParticular selectionModel
1998
Diagnosing breast cancer from FNAs: variable relevance in neural network and logistic regression models.
Ohno-Machado L, Bialek D. Diagnosing breast cancer from FNAs: variable relevance in neural network and logistic regression models. 1998, 52 Pt 1: 537-40. PMID: 10384515.Peer-Reviewed Original ResearchImproving 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 ResearchBuilding manageable rough set classifiers.
Ohrn A, Ohno-Machado L, Rowland T. Building manageable rough set classifiers. AMIA Annual Symposium Proceedings 1998, 543-7. PMID: 9929278, PMCID: PMC2232320.Peer-Reviewed Original ResearchConceptsReal-world medical datasetsRule-based classifierRough set classifierRough set theoryKnowledge discoveryData miningMedical datasetsBoolean reasoningSet classifierSet theoryClassifierBetter performanceSmall modelsMiningAvailable informationDatasetReasoningInteresting aspectsModelCapabilityInformationSetInspectionRulesPerformance
1997
Sequential versus standard neural networks for pattern recognition: An example using the domain of coronary heart disease
Ohno-Machado L, Musen M. Sequential versus standard neural networks for pattern recognition: An example using the domain of coronary heart disease. Computers In Biology And Medicine 1997, 27: 267-281. PMID: 9303265, DOI: 10.1016/s0010-4825(97)00008-5.Peer-Reviewed Original ResearchMeSH KeywordsAdultAge FactorsAlgorithmsArea Under CurveBlood PressureBody WeightCause of DeathCholesterolCoronary DiseaseDatabases as TopicDemographyDisease ProgressionDisease-Free SurvivalEvaluation Studies as TopicFollow-Up StudiesForecastingHumansMaleMiddle AgedModels, CardiovascularNeural Networks, ComputerOutcome Assessment, Health CarePattern Recognition, AutomatedPrognosisROC CurveSmokingSurvival AnalysisTime FactorsConceptsNeural network modelNeural networkSequential neural network modelsTime-oriented dataNetwork modelNeural network architectureStandard neural networkSequential neural networkNeural network systemRecognition of patternsNetwork architecturePattern recognitionUnseen casesNetwork systemTest setSingle pointResearch data basesData basesNetworkMedical researchersSuch modelsRecognitionBackpropagationSetArchitecture
1995
Learning rare categories in backpropagation
Ohno-Machado L, Musen M. Learning rare categories in backpropagation. Lecture Notes In Computer Science 1995, 991: 201-209. DOI: 10.1007/bfb0034813.Peer-Reviewed Original ResearchHierarchical neural networks for survival analysis.
Ohno-Machado L, Walker M, Musen M. Hierarchical neural networks for survival analysis. Medinfo. 1995, 8 Pt 1: 828-32. PMID: 8591339.Peer-Reviewed Original ResearchConceptsNeural networkHierarchical neural networkHierarchical systemHierarchical modelHierarchical architectureDiscrete variablesNetworkData setsNonhierarchical modelTraditional methodsMedical applicationsAccurate predictionNumber of eventsArchitectureSystemTime-dependent variablesModelDataFirst time intervalTime intervalPredictionSetVariables