2019
Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records
Baxter S, Marks C, Kuo T, Ohno-Machado L, Weinreb R. Machine Learning-Based Predictive Modeling of Surgical Intervention in Glaucoma Using Systemic Data From Electronic Health Records. American Journal Of Ophthalmology 2019, 208: 30-40. PMID: 31323204, PMCID: PMC6888922, DOI: 10.1016/j.ajo.2019.07.005.Peer-Reviewed Original ResearchConceptsPrimary open-angle glaucomaElectronic health recordsMultivariable logistic regressionSurgical interventionGlaucoma surgeryPOAG patientsSystemic dataHigher mean systolic blood pressureMean systolic blood pressureNon-opioid analgesic medicationsLogistic regressionCertain medication classesEye-specific dataHealth recordsRisk of progressionSystolic blood pressureOpen-angle glaucomaSingle academic institutionAnti-hyperlipidemic medicationsAnalgesic medicationMedication classesProgressive diseaseBlood pressureCalcium blockersOphthalmic medications
2003
Microarrays and clinical dentistry
Kuo W, Whipple M, Jenssen T, Todd R, Epstein J, Ohno-Machado L, Sonis S, Park P. Microarrays and clinical dentistry. The Journal Of The American Dental Association 2003, 134: 456-462. PMID: 12733779, DOI: 10.14219/jada.archive.2003.0195.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsAdvanced microarray technologiesEntire human genomeActivity of genesGene expression patternsHuman Genome ProjectGenome levelHuman genomeGenetic approachesCellular developmentDNA sequencesGenome ProjectExciting biologyCandidate genesExpression patternsMicroarray technologyMicroarrayNumber of cancersGenesMolecular profileAnalysis of diseasesMolecular behaviorGenomeBiologyTissue samplesProtein
2002
Gene expression levels in different stages of progression in oral squamous cell carcinoma.
Kuo W, Jenssen T, Park P, Lingen M, Hasina R, Ohno-Machado L. Gene expression levels in different stages of progression in oral squamous cell carcinoma. AMIA Annual Symposium Proceedings 2002, 415-9. PMID: 12474876, PMCID: PMC2244435.Peer-Reviewed Original ResearchMeSH KeywordsCarcinoma, Squamous CellDisease ProgressionGene ExpressionHumansMouth NeoplasmsNeoplasm StagingConceptsOral squamous cell carcinomaExpression levelsGene expression studiesGene expression dataGene expression levelsChromosome domainsCancer samplesExpression studiesSquamous cell carcinomaMolecular mechanismsExpression dataGenesMolecular levelProgression of OSCCCell carcinomaGenetic featuresCancer typesCommon cancer typesImportant insightsSignificant differencesSmall panelPatient samplesDifferent stagesProgressionSample types
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 ResearchMeSH KeywordsAcquired Immunodeficiency SyndromeDisease ProgressionHumansNeural Networks, ComputerPrognosisProportional Hazards ModelsSoftwareSurvival AnalysisConceptsCox 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