2015
A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research
Meeker D, Jiang X, Matheny M, Farcas C, D’Arcy M, Pearlman L, Nookala L, Day M, Kim K, Kim H, Boxwala A, El-Kareh R, Kuo G, Resnic F, Kesselman C, Ohno-Machado L. A system to build distributed multivariate models and manage disparate data sharing policies: implementation in the scalable national network for effectiveness research. Journal Of The American Medical Informatics Association 2015, 22: 1187-1195. PMID: 26142423, PMCID: PMC4639714, DOI: 10.1093/jamia/ocv017.Peer-Reviewed Original ResearchConceptsFederated networkData sharing policiesParallel computation methodSharing policiesPolicy management systemData exchange policiesData storage requirementsWeb servicesNetwork queriesQuery functionalityComputation resourcesFederated modelGraphical interfaceData transportCentralized networkStorage requirementsNetwork participantsManagement systemQueriesNetworkComputation methodNew featuresMultivariate statistical estimationDifferent state lawsImportant new featureComparison of consumers’ views on electronic data sharing for healthcare and research
Kim K, Joseph J, Ohno-Machado L. Comparison of consumers’ views on electronic data sharing for healthcare and research. Journal Of The American Medical Informatics Association 2015, 22: 821-830. PMID: 25829461, PMCID: PMC5009901, DOI: 10.1093/jamia/ocv014.Peer-Reviewed Original ResearchConceptsElectronic data sharingData sharingHealth information exchangeData networksHealth informationTechnology infrastructureInformation exchangePrivacyHealth Insurance PortabilitySharingAccountability ActUse of dataInsurance PortabilitySecurityNetworkInformationHealthcareIndividual controlHealthcare deliveryMere relianceDepth studyPortabilityAccessInfrastructureComparison of consumersRanking Medical Subject Headings using a factor graph model.
Wei W, Demner-Fushman D, Wang S, Jiang X, Ohno-Machado L. Ranking Medical Subject Headings using a factor graph model. AMIA Joint Summits On Translational Science Proceedings 2015, 2015: 56-63. PMID: 26306236, PMCID: PMC4525219.Peer-Reviewed Original ResearchMean average precisionHighest mean average precisionFactor graph modelNovel data-driven approachActive research topicData-driven approachMeSH ontologyAverage precisionGraphical modelsSemantic distanceGraph modelResearch topicCitation networkNational LibraryOntologyMedical Subject HeadingsSubject headingsNetworkPreliminary resultsRecent workScenariosLibraryWork
2014
pSCANNER: patient-centered Scalable National Network for Effectiveness Research
Ohno-Machado L, Agha Z, Bell D, Dahm L, Day M, Doctor J, Gabriel D, Kahlon M, Kim K, Hogarth M, Matheny M, Meeker D, Nebeker J, team T, Resnic F, Khodyakov D, Armstead L, Nagler T, Morley S, Anderson N, Cooper D, Phillips D, Heber D, Li Z, Ong M, Patel A, Zachariah M, Burns J, Daniels L, Doan S, Farcas C, Germann-Kurtz R, Jiang X, Kim H, Paul P, Taras H, Tremoulet A, Wang S, Zhu W, Berman D, Rizk-Jackson A, D’Arcy M, Kesselman C, Knight T, Pearlman L, Heidenreich P, Rifkin D, Stepnowsky C, Zamora T, DuVall S, Frey L, Scehnet J, Sauer B, Facelli J, Gouripeddi R, Denton J, FitzHenry F, Fly J, Messina V, Minter F, Nookala L, Sullivan H, Speroff T, Westerman D. pSCANNER: patient-centered Scalable National Network for Effectiveness Research. Journal Of The American Medical Informatics Association 2014, 21: 621-626. PMID: 24780722, PMCID: PMC4078293, DOI: 10.1136/amiajnl-2014-002751.Peer-Reviewed Original ResearchConceptsInitial use caseCommon data modelPatient Centered Outcomes Research InstituteHealth information exchange dataComputing infrastructuresFederated networkUse casesDistributed systemData modelComputation modelExchange dataPatient-powered research networksCommunity-based outpatient clinicsNetworkCongestive heart failureOutcomes Research InstituteEffectiveness researchAsynchronous modeHealth services researchersKawasaki diseaseHeart failureOutpatient clinicVA InformaticsAmbulatory careNational networkChapter 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
Data governance requirements for distributed clinical research networks: triangulating perspectives of diverse stakeholders
Kim K, Browe D, Logan H, Holm R, Hack L, Ohno-Machado L. Data governance requirements for distributed clinical research networks: triangulating perspectives of diverse stakeholders. Journal Of The American Medical Informatics Association 2013, 21: 714-719. PMID: 24302285, PMCID: PMC4078279, DOI: 10.1136/amiajnl-2013-002308.Peer-Reviewed Original ResearchConceptsFair Information Practice PrinciplesTechnical infrastructureClinical data reuseGovernance requirementsTrustworthy platformData reuseHealth Insurance PortabilityHIPAA regulationsAccountability ActNetworkInsurance PortabilityHealth informationRequirementsInformationResearch NetworkPrivacyPortabilityBest practicesInfrastructurePlatformReuseDiverse stakeholdersTimeliness
2011
Feasibility Evaluation of Smart Stretcher to Improve Patient Safety during Transfers
Ohashi K, Kurihara Y, Watanabe K, Ohno-Machado L, Tanaka H. Feasibility Evaluation of Smart Stretcher to Improve Patient Safety during Transfers. Methods Of Information In Medicine 2011, 50: 253-264. PMID: 21057715, DOI: 10.3414/me0616.Peer-Reviewed Original ResearchConceptsSmart StretcherWireless networksIntelligent alarm systemWireless sensor networksNetwork-based systemZigBee wireless networkVital sign sensorsSensor networksRFID technologyIndoor positioning systemPatient IDPatient identification systemPatient's vital signsIdentification systemSimulated hospital environmentReal timeAlarm systemType pressure sensorApnea detectionFeasibility experimentsNetworkPressure sensorPositioning systemTechnical feasibility studySubjective evaluation
2004
Prediction of mortality in an Indian intensive care unit
Nimgaonkar A, Karnad D, Sudarshan S, Ohno-Machado L, Kohane I. Prediction of mortality in an Indian intensive care unit. Intensive Care Medicine 2004, 30: 248-253. PMID: 14727015, DOI: 10.1007/s00134-003-2105-4.Peer-Reviewed Original ResearchConceptsNeural networkIndian data setAPACHE IIArtificial neural network modelBack-propagation algorithmNeural network modelAnalysis of informationDay 1 APACHE II scoreIndian Intensive Care UnitsNetwork modelAPACHE II equationAPACHE II systemAPACHE II scoreIntensive care unitRisk of deathPrediction of mortalityNetworkHosmer-Lemeshow statisticData setsLogistic regression modelsHospital outcomesII scoreCare unitUniversity HospitalConsecutive admissions
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 StatementsA 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
2000
Risk stratification in heart failure using artificial neural networks.
Atienza F, Martinez-Alzamora N, De Velasco J, Dreiseitl S, Ohno-Machado L. Risk stratification in heart failure using artificial neural networks. AMIA Annual Symposium Proceedings 2000, 32-6. PMID: 11079839, PMCID: PMC2243942.Peer-Reviewed Original ResearchConceptsNeural network modelNeural networkNetwork modelMedical classification problemsArtificial neural networkSimple neural networkHeart failureAutomatic relevance determination (ARD) methodClassification problemRisk stratificationOne-year event-free survivalOne-year prognosisEvent-free survivalAccurate risk stratificationHeart failure patientsComplex multisystem diseaseNetworkFailure patientsMultisystem diseaseResampling methodPatientsPrognosisOutcomesPredictorsFailure
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 StatementsEvaluating 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 framework and tools for authoring, editing, documenting, sharing, searching, navigating, and executing computer-based clinical guidelines.
Greenes R, Boxwala A, Sloan W, Ohno-Machado L, Deibel S. A framework and tools for authoring, editing, documenting, sharing, searching, navigating, and executing computer-based clinical guidelines. AMIA Annual Symposium Proceedings 1999, 261-5. PMID: 10566361, PMCID: PMC2232593.Peer-Reviewed Original ResearchConceptsComputer-based clinical guidelineInterMed CollaboratoryAutomatic executionSoftware toolsGuideline representationDelivery networkInternet distributionSharingSpecificationXMLFrameworkGLIFNavigationExecutionCollaboratoryToolNetworkImplementationRepresentationSuiteEditingEligibility determinationDocumenting
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 ResearchDiagnosing 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 Research
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