2021
Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review
Nguyen N, Picetti D, Dulai P, Jairath V, Sandborn W, Ohno-Machado L, Chen P, Singh S. Machine Learning-based Prediction Models for Diagnosis and Prognosis in Inflammatory Bowel Diseases: A Systematic Review. Journal Of Crohn's And Colitis 2021, 16: 398-413. PMID: 34492100, PMCID: PMC8919806, DOI: 10.1093/ecco-jcc/jjab155.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsInflammatory bowel diseaseBowel diseaseClinical dataHigh riskRisk predictionSystematic reviewAcute severe ulcerative colitisLongitudinal disease activitySevere ulcerative colitisAdverse clinical outcomesBias assessment toolRisk of biasAvailable clinical dataMachine learning-based prediction modelsPrediction model RiskDisease activityCohort studyUlcerative colitisClinical outcomesTreatment responseClinical applicabilityLearning-based prediction modelsExternal validationPatientsRisk
2020
EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning
Kuo T, Gabriel R, Cidambi K, Ohno-Machado L. EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning. Journal Of The American Medical Informatics Association 2020, 27: 747-756. PMID: 32364235, PMCID: PMC7309256, DOI: 10.1093/jamia/ocaa023.Peer-Reviewed Original ResearchConceptsBlockchain technologyCentral serverServer-based methodBenefits of blockchainData protection policiesCentralized serverArtificial intelligenceModel learningDecentralized approachSmall datasetsBlockchainServerComputation strategySingle pointGeneralizable modelCost of efficiencyGenomic datasetsDatasetDistributed modelTechnologyGenomic dataMultiple institutionsDiscrimination powerIntelligencePotential advantages/disadvantages
2012
Selecting cases for whom additional tests can improve prognostication.
Jiang X, Kim J, Wu Y, Wang S, Ohno-Machado L. Selecting cases for whom additional tests can improve prognostication. AMIA Annual Symposium Proceedings 2012, 2012: 1260-8. PMID: 23304404, PMCID: PMC3540468.Peer-Reviewed Original Research
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
Discrimination and calibration of mortality risk prediction models in interventional cardiology
Matheny M, Ohno-Machado L, Resnic F. Discrimination and calibration of mortality risk prediction models in interventional cardiology. Journal Of Biomedical Informatics 2005, 38: 367-375. PMID: 16198996, DOI: 10.1016/j.jbi.2005.02.007.Peer-Reviewed Original ResearchMeSH KeywordsAngioplasty, Balloon, CoronaryCalibrationCardiologyComorbidityDecision Support Systems, ClinicalDiagnosis, Computer-AssistedDiscriminant AnalysisExpert SystemsHumansIncidenceOutcome Assessment, Health CarePostoperative ComplicationsPrognosisRetrospective StudiesRisk AssessmentRisk FactorsROC CurveSurvival AnalysisSurvival RateUnited StatesConceptsLocal risk modelAcute myocardial infarctionHosmer-Lemeshow goodnessRisk prediction modelRisk factorsCardiology-National Cardiovascular Data RegistryConsecutive percutaneous coronary interventionsMortality risk prediction modelPercutaneous coronary interventionMultivariate risk factorsCertain risk factorsROC curveAccurate risk predictionIndividual casesGood discriminationCardiogenic shockHospital mortalityCoronary interventionUnstable anginaArtery interventionPatient populationMyocardial infarctionRisk modelElective proceduresWomen's HospitalA global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method
Zou K, Resnic F, Talos I, Goldberg-Zimring D, Bhagwat J, Haker S, Kikinis R, Jolesz F, Ohno-Machado L. A global goodness-of-fit test for receiver operating characteristic curve analysis via the bootstrap method. Journal Of Biomedical Informatics 2005, 38: 395-403. PMID: 16198998, DOI: 10.1016/j.jbi.2005.02.004.Peer-Reviewed Original ResearchAdolescentAdultAlgorithmsAngioplasty, Balloon, CoronaryBrain NeoplasmsCalibrationData Interpretation, StatisticalDecision Support Systems, ClinicalDiagnosis, Computer-AssistedDiscriminant AnalysisExpert SystemsFemaleHumansIncidenceMaleMiddle AgedOutcome Assessment, Health CarePrognosisRisk AssessmentRisk FactorsROC CurveSurvival AnalysisSurvival RateUnited States
2004
Deciphering gene expression profiles generated from DNA microarrays and their applications in oral medicine
Kuo W, Whipple M, Epstein J, Jenssen T, Santos G, Ohno-Machado L, Sonis S. Deciphering gene expression profiles generated from DNA microarrays and their applications in oral medicine. Oral Surgery Oral Medicine Oral Pathology And Oral Radiology 2004, 97: 584-591. PMID: 15153870, DOI: 10.1016/j.tripleo.2003.11.016.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsGene expression profilesTranscriptional mappingDNA microarraysExpression profilesGenome-wide monitoringThousands of genesApplication of microarraysTypical microarray experimentTranscription levelsBiological processesGenetic changesMicroarray technologyMicroarray experimentsDiseased cellsMicroarrayRelative expressionDisease etiologyNew therapeutic toolsWidespread hopeCellsGenesNew biomarkersTherapeutic toolExpressionDiscovery
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 StatementsSimplified risk score models accurately predict the risk of major in-hospital complications following percutaneous coronary intervention
Resnic F, Ohno-Machado L, Selwyn A, Simon D, Popma J. Simplified risk score models accurately predict the risk of major in-hospital complications following percutaneous coronary intervention. The American Journal Of Cardiology 2001, 88: 5-9. PMID: 11423050, DOI: 10.1016/s0002-9149(01)01576-4.Peer-Reviewed Original ResearchMeSH KeywordsAngioplasty, Balloon, CoronaryCardiopulmonary BypassCoronary DiseaseFemaleHospital MortalityHumansLogistic ModelsMaleMiddle AgedMyocardial InfarctionNeural Networks, ComputerPostoperative ComplicationsPredictive Value of TestsPrognosisProspective StudiesRisk AssessmentRisk FactorsROC CurveConceptsPercutaneous coronary interventionRisk score modelHospital complicationsCoronary interventionScore modelGlycoprotein IIb/IIIa antagonistsIIb/IIIa antagonistsMultiple logistic regression modelBedside risk stratificationCombined end pointUse of stentsMultiple logistic modelLogistic regression modelsBypass surgeryRisk stratificationSingle centerDecreased riskMyocardial infarctionRisk factorsComplicationsInterventional proceduresEnd pointLogistic regressionLogistic modelROC curveEffects 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
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 methodPatientsPrognosisOutcomesPredictorsFailureMajor complications after angioplasty in patients with chronic renal failure: a comparison of predictive models.
Lacson R, Ohno-Machado L. Major complications after angioplasty in patients with chronic renal failure: a comparison of predictive models. AMIA Annual Symposium Proceedings 2000, 457-61. PMID: 11079925, PMCID: PMC2243840.Peer-Reviewed Original ResearchConceptsPercutaneous transluminal coronary angioplastyEnd-stage renal diseaseChronic renal failureMajor complicationsRenal failureCongestive heart failurePatient risk factorsTransluminal coronary angioplastyLogistic regression modelsCoronary angioplastyHeart failureRenal diseasePoor outcomeMyocardial infarctionRisk factorsPrior historyPresence of shockComplicationsLogistic regressionPatientsDiscriminatory abilityDemographic characteristicsAngioplastyRegression modelsStandard logistic regression modelDevelopment and evaluation of models to predict death and myocardial infarction following coronary angioplasty and stenting.
Resnic F, Popma J, Ohno-Machado L. Development and evaluation of models to predict death and myocardial infarction following coronary angioplasty and stenting. AMIA Annual Symposium Proceedings 2000, 690-3. PMID: 11079972, PMCID: PMC2243827.Peer-Reviewed Original ResearchConceptsPercutaneous coronary interventionMyocardial infarctionPost-procedural myocardial infarctionMajor acute complicationsRisk-scoring systemRisk of deathMultivariate logistic regressionRisk score modelRisk-scoring modelLogistic regression modelsAcute complicationsCoronary angioplastyCoronary interventionClinical practiceInfarctionScoring systemInterventional cardiologyLogistic regressionRisk modelClinical implementationRisk deathDeathScore modelReasonable discriminationRegression models
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 Statements
1998
Comparison of multiple prediction models for ambulation following spinal cord injury.
Rowland T, Ohno-Machado L, Ohrn A. Comparison of multiple prediction models for ambulation following spinal cord injury. AMIA Annual Symposium Proceedings 1998, 528-32. PMID: 9929275, PMCID: PMC2232380.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 modelsRecognitionBackpropagationSetArchitectureA comparison of Cox proportional hazards and artificial neural network models for medical prognosis
Ohno-Machado L. A comparison of Cox proportional hazards and artificial neural network models for medical prognosis. Computers In Biology And Medicine 1997, 27: 55-65. PMID: 9055046, DOI: 10.1016/s0010-4825(96)00036-4.Peer-Reviewed Original ResearchConceptsCox proportional hazardsCox modelDisease progressionProportional hazardsCox proportional hazards modelDiagnosis of AIDSProportional hazards modelNegative predictive valuePrognostic accuracyIndividual patientsStudy populationPrognostic toolHazards modelPredictive valuePatientsPrognosisAIDSCharacteristic curveMedical prognosisProgressionPractice of medicineAccurate assessment
1996
Sequential use of neural networks for survival prediction in AIDS.
Ohno-Machado L. Sequential use of neural networks for survival prediction in AIDS. AMIA Annual Symposium Proceedings 1996, 170-4. PMID: 8947650, PMCID: PMC2233186.Peer-Reviewed Original Research
1995
A comparison of two computer-based prognostic systems for AIDS.
Ohno-Machado L, Musen M. A comparison of two computer-based prognostic systems for AIDS. AMIA Annual Symposium Proceedings 1995, 737-41. PMID: 8563387, PMCID: PMC2579191.Peer-Reviewed Original ResearchConceptsCox modelPredictive valueCox proportional hazards modelDiagnosis of AIDSThree-year survivalProportional hazards modelCohort of peopleNegative predictive valuePositive predictive valueDisease progressionPrognostic accuracyStudy populationHazards modelIndividualized estimatesPrognostic toolProbability of survivalPatientsAIDSPrognostic systemCohortFirst yearDiagnosisDeathSurvivalYears