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
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 methods
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 ResearchComparison 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 modelsRecognitionBackpropagationSetArchitecture
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
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
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 ResearchConceptsHierarchical 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