2023
Chapter 7 Data-driven approaches to generating knowledge: Machine learning, artificial intelligence, and predictive modeling
Matheny M, Ohno-Machado L, Davis S, Nemati S. Chapter 7 Data-driven approaches to generating knowledge: Machine learning, artificial intelligence, and predictive modeling. 2023, 217-255. DOI: 10.1016/b978-0-323-91200-6.00031-0.Peer-Reviewed Original Research
2021
VERTIcal Grid lOgistic regression with Confidence Intervals (VERTIGO-CI).
Kim J, Li W, Bath T, Jiang X, Ohno-Machado L. VERTIcal Grid lOgistic regression with Confidence Intervals (VERTIGO-CI). AMIA Joint Summits On Translational Science Proceedings 2021, 2021: 355-364. PMID: 34457150, PMCID: PMC8378611.Peer-Reviewed Original ResearchConceptsDual spaceCovariance matrixVariance estimationSpace modelTest statisticKernel matrixLinear modelReal dataTolerable performanceNovel extensionDual-space modelPoint estimatesEquivalent accuracyCentralized versionStatisticsRegression modelsModelMatrixExtensionDual objectivesCentralized settingFederated LearningEstimationPrivacy-preserving mannerSpace
2016
- Bayesian ROC Methods
Zou K, Liu A, Bandos A, Ohno-Machado L, Rockette H. - Bayesian ROC Methods. 2016, 102-121. DOI: 10.1201/b11031-8.Peer-Reviewed Original Research
2014
Chapter 11 Generation of Knowledge for Clinical Decision Support Statistical and Machine Learning Techniques
Matheny M, Ohno-Machado L. Chapter 11 Generation of Knowledge for Clinical Decision Support Statistical and Machine Learning Techniques. 2014, 309-337. DOI: 10.1016/b978-0-12-398476-0.00011-7.ChaptersMachine learning techniquesClinical decision support systemArtificial neural networkDecision support systemModeling methodLearning techniquesMedical domainAlternative modeling methodNeural networkSupport systemPopular exampleClassification treesKnowledge generationNetworkStatisticalModelMethodGeneration
2013
EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed privacy-preserving online model learning
Wang S, Jiang X, Wu Y, Cui L, Cheng S, Ohno-Machado L. EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed privacy-preserving online model learning. Journal Of Biomedical Informatics 2013, 46: 480-496. PMID: 23562651, PMCID: PMC3676314, DOI: 10.1016/j.jbi.2013.03.008.Peer-Reviewed Original ResearchConceptsHigh-level guaranteesOnline model learningSensitive informationModel learningEntire dataOnline learningAbsence of participantsMore flexibilitySame performanceExperimental resultsLearningCommunicationServerInformationGuaranteesModel updatingPosterior distributionServicesClientsUpdatingFrameworkFlexibilityModelPerformance
2012
Preserving Institutional Privacy in Distributed binary Logistic Regression.
Wu Y, Jiang X, Ohno-Machado L. Preserving Institutional Privacy in Distributed binary Logistic Regression. AMIA Annual Symposium Proceedings 2012, 2012: 1450-8. PMID: 23304425, PMCID: PMC3540539.Peer-Reviewed Original ResearchGrid 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
2007
10 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
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
Modeling Medical Prognosis: Survival Analysis Techniques
Ohno-Machado L. Modeling Medical Prognosis: Survival Analysis Techniques. Journal Of Biomedical Informatics 2001, 34: 428-439. PMID: 12198763, DOI: 10.1006/jbin.2002.1038.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus Statements
1999
NEURAL NETWORK APPLICATIONS IN PHYSICAL MEDICINE AND REHABILITATION1
Ohno-Machado L, Rowland T. NEURAL NETWORK APPLICATIONS IN PHYSICAL MEDICINE AND REHABILITATION1. American Journal Of Physical Medicine & Rehabilitation 1999, 78: 392-398. PMID: 10418849, DOI: 10.1097/00002060-199907000-00022.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsA 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
1995
Hierarchical 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