2023
Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance
Davis S, Ssemaganda H, Koola J, Mao J, Westerman D, Speroff T, Govindarajulu U, Ramsay C, Sedrakyan A, Ohno-Machado L, Resnic F, Matheny M. Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance. BMC Medical Research Methodology 2023, 23: 89. PMID: 37041457, PMCID: PMC10088292, DOI: 10.1186/s12874-023-01913-9.Peer-Reviewed Original ResearchConceptsSynthetic datasetsData characteristicsFeature distributionGround truthMIMIC-III dataReal-world dataData generation processComplex simulation studiesData relationshipsUser definitionSmall datasetsSimulation requirementsCorrelated featuresWorld dataCustomizable optionsReal-world complexitySynthetic patientsNew algorithmDatasetGeneration processLearningAlgorithmData simulation techniquesLearning effectGeneralizable framework
2014
Differentially private distributed logistic regression using private and public data
Ji Z, Jiang X, Wang S, Xiong L, Ohno-Machado L. Differentially private distributed logistic regression using private and public data. BMC Medical Genomics 2014, 7: s14. PMID: 25079786, PMCID: PMC4101668, DOI: 10.1186/1755-8794-7-s1-s14.Peer-Reviewed Original ResearchConceptsPrivate dataDifferential privacyPublic datasetsPublic dataRigorous privacy guaranteeData privacy researchPrivate data setsData mining modelsData setsProvable privacyPrivacy guaranteesMining modelPrivacy researchDifferent data setsArt frameworksMedical informaticsPrivacyAmount of noisePrivate methodsAuxiliary informationBetter utilityNew algorithmUpdate stepAvailable public dataAlgorithm
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