2018
Data science and artificial intelligence to improve clinical practice and research
Ohno-Machado L. Data science and artificial intelligence to improve clinical practice and research. Journal Of The American Medical Informatics Association 2018, 25: 1273-1273. PMID: 30312446, PMCID: PMC7646927, DOI: 10.1093/jamia/ocy136.Commentaries, Editorials and Letters
2013
Comparison of four prediction models to discriminate benign from malignant vertebral compression fractures according to MRI feature analysis.
Thawait S, Kim J, Klufas R, Morrison W, Flanders A, Carrino J, Ohno-Machado L. Comparison of four prediction models to discriminate benign from malignant vertebral compression fractures according to MRI feature analysis. American Journal Of Roentgenology 2013, 200: 493-502. PMID: 23436836, DOI: 10.2214/ajr.11.7192.Peer-Reviewed Original ResearchAdultAgedAged, 80 and overAlgorithmsCohort StudiesComputer SimulationDiagnosis, DifferentialFemaleFractures, CompressionHumansImage EnhancementImage Interpretation, Computer-AssistedMagnetic Resonance ImagingMaleMiddle AgedModels, BiologicalNeoplasmsPattern Recognition, AutomatedReproducibility of ResultsSensitivity and SpecificitySpinal FracturesYoung Adult
2012
Grid 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 ResearchMeSH KeywordsArea Under CurveBiomedical ResearchComputer SimulationConfidentialityHumansInformation DisseminationLogistic ModelsPattern Recognition, AutomatedROC CurveConceptsIntegrity of communicationCentralized data sourcesTraditional LR modelCentral repositoryComputational costData sourcesData setsSame formatPatient dataComputationGenomic dataRare patternRelevant dataLR modelPrediction valueSetRepositoryPartial elementsFormatClassificationCommunicationModelDataPatient setPerform
2011
Natural language processing: an introduction
Nadkarni PM, Ohno-Machado L, Chapman WW. Natural language processing: an introduction. Journal Of The American Medical Informatics Association 2011, 18: 544-551. PMID: 21846786, PMCID: PMC3168328, DOI: 10.1136/amiajnl-2011-000464.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsMeSH KeywordsHumansInformation ManagementInformation Storage and RetrievalMedical InformaticsModels, TheoreticalNatural Language ProcessingPattern Recognition, AutomatedUser-Computer Interface
2005
Small, fuzzy and interpretable gene expression based classifiers
Vinterbo S, Kim E, Ohno-Machado L. Small, fuzzy and interpretable gene expression based classifiers. Bioinformatics 2005, 21: 1964-1970. PMID: 15661797, DOI: 10.1093/bioinformatics/bti287.Peer-Reviewed Original Research
2004
A primer on gene expression and microarrays for machine learning researchers
Kuo W, Kim E, Trimarchi J, Jenssen T, Vinterbo S, Ohno-Machado L. A primer on gene expression and microarrays for machine learning researchers. Journal Of Biomedical Informatics 2004, 37: 293-303. PMID: 15465482, DOI: 10.1016/j.jbi.2004.07.002.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsNew algorithmSupervised learning modelUCI machineLearning modelMicroarray data analysisAlgorithmic developmentsTypes of dataMachineData setsMain challengesGene expression dataMain motivationAlgorithmData analysisBiomedical experimentsLarge numberExpression dataMicroarray dataResearchersRepositoryWebMicroarray experimentsNew waveDataSetThe 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
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