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 StatementsMeSH KeywordsArtificial IntelligenceCritical CareDecision Support Systems, ClinicalDiagnosis, Computer-AssistedHealth Status IndicatorsHumansPrognosisProportional Hazards ModelsRisk AssessmentRisk Factors
2005
The use of receiver operating characteristic curves in biomedical informatics
Lasko T, Bhagwat J, Zou K, Ohno-Machado L. The use of receiver operating characteristic curves in biomedical informatics. Journal Of Biomedical Informatics 2005, 38: 404-415. PMID: 16198999, DOI: 10.1016/j.jbi.2005.02.008.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsDiscrimination 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
The Goodman-Kruskal coefficient and its applications in genetic diagnosis of cancer
Jaroszewicz S, Simovici D, Kuo W, Ohno-Machado L. The Goodman-Kruskal coefficient and its applications in genetic diagnosis of cancer. IEEE Transactions On Biomedical Engineering 2004, 51: 1095-1102. PMID: 15248526, DOI: 10.1109/tbme.2004.827267.Peer-Reviewed Original Research
2001
A 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 ResearchMeSH KeywordsAlgorithmsDecision TreesDiagnosis, Computer-AssistedHumansLogistic ModelsMelanomaNeural Networks, ComputerNevusNevus, PigmentedSkin DiseasesSkin NeoplasmsSkin PigmentationConceptsArtificial neural networkDichotomous problemNearest neighborsDifferent classification tasksSpecific classification problemMachine learning methodsMachine-learning methodsClassification taskClassification problemNeural networkLearning methodsDecision tressPigmented skin lesionsVector machineDecision treeTaskNeighborsSVMMachineNetworkBenchmarksCommon neviMethodExcellent resultsUsing patient-reportable clinical history factors to predict myocardial infarction
Wang S, Ohno-Machado L, Fraser H, Kennedy R. Using patient-reportable clinical history factors to predict myocardial infarction. Computers In Biology And Medicine 2001, 31: 1-13. PMID: 11058690, DOI: 10.1016/s0010-4825(00)00022-6.Peer-Reviewed Original Research
1999
The decision systems group: creating a framework for decision making.
Greenes R, Boxwala A, Ohno-Machado L. The decision systems group: creating a framework for decision making. M.D. Computing 1999, 16: 23-7. PMID: 10507232.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 ResearchMeSH KeywordsAlgorithmsArtificial IntelligenceChest PainDiagnosis, Computer-AssistedEvaluation Studies as TopicHumansLogistic ModelsMathematicsMyocardial InfarctionNeural Networks, ComputerConceptsMachine-learning techniquesBayesian neural networksNeural networkMultilayer perceptronRough setsVariable selection methodsSelection methodInput variablesVariable selectionInfarction dataBackpropagationPerceptronMyocardial infarction dataDifferent subsetsAlgorithmNetworkMethodSetDifferent methods
1994
Identification of low frequency patterns in backpropagation neural networks.
Ohno-Machado L. Identification of low frequency patterns in backpropagation neural networks. AMIA Annual Symposium Proceedings 1994, 853-9. PMID: 7950045, PMCID: PMC2247950.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsClassificationDiagnosis, Computer-AssistedHumansNeural Networks, ComputerSensitivity and SpecificityThyroid DiseasesConceptsHierarchical neural networkStandard neural networkNeural networkInfrequent patternsTriage NetworkNeural network systemBackpropagation neural networkSame time constraintsReal data setsConquer approachArtificial setNetworkSupersetTime constraintsData setsReal setSpecialized networksPrediction powerSetPattern similarityRare classPrior probabilityRecent yearsSystemFrequency patterns