2020
Active Surveillance of the Implantable Cardioverter-Defibrillator Registry for Defibrillator Lead Failures
Resnic F, Majithia A, Dhruva S, Ssemaganda H, Robbins S, Marinac-Dabic D, Hewitt K, Ohno-Machado L, Reynolds M, Matheny M. Active Surveillance of the Implantable Cardioverter-Defibrillator Registry for Defibrillator Lead Failures. Circulation Cardiovascular Quality And Outcomes 2020, 13: e006105. PMID: 32283971, PMCID: PMC7360169, DOI: 10.1161/circoutcomes.119.006105.Peer-Reviewed Original ResearchConceptsICD RegistryLead failureActive surveillanceNational Cardiovascular Data Registry ICD RegistryImplantable Cardioverter-Defibrillator RegistryPrimary safety end pointPropensity-matched survival analysisRate of freedomSafety end pointLead failure rateLong-term safetySignificant patient harmDefibrillator lead failureEarly lead failureMonitoring of safetyComparator patientsContemporary ICDLead survivalMeaningful differencesOutcome ascertainmentFailure rateNew ICDPatient harmPatientsSurvival analysisPromoting Quality Face-to-Face Communication during Ophthalmology Encounters in the Electronic Health Record Era
Baxter S, Gali H, Chiang M, Hribar M, Ohno-Machado L, El-Kareh R, Huang A, Chen H, Camp A, Kikkawa D, Korn B, Lee J, Longhurst C, Millen M. Promoting Quality Face-to-Face Communication during Ophthalmology Encounters in the Electronic Health Record Era. Applied Clinical Informatics 2020, 11: 130-141. PMID: 32074650, PMCID: PMC7030957, DOI: 10.1055/s-0040-1701255.Peer-Reviewed Original ResearchConceptsElectronic Health Record EraDocumentation efficiencyEHR implementationPhysician-patient interactionAcademic ophthalmology departmentsElectronic health recordsOutpatient ophthalmologyQuality improvement strategiesUse of scribesOutpatient encountersProspective studyOphthalmology departmentQI interventionsQI strategiesPatientsTeam-based workflowsEHR efficiencyNote templateArray of interventionsOphthalmologistsEHR useHealth recordsDocumentation timePhysician burnoutTablet-based application
2019
Time Requirements of Paper-Based Clinical Workflows and After-Hours Documentation in a Multispecialty Academic Ophthalmology Practice
Baxter S, Gali H, Huang A, Millen M, El-Kareh R, Nudleman E, Robbins S, Heichel C, Camp A, Korn B, Lee J, Kikkawa D, Longhurst C, Chiang M, Hribar M, Ohno-Machado L. Time Requirements of Paper-Based Clinical Workflows and After-Hours Documentation in a Multispecialty Academic Ophthalmology Practice. American Journal Of Ophthalmology 2019, 206: 161-167. PMID: 30910517, PMCID: PMC6755078, DOI: 10.1016/j.ajo.2019.03.014.Peer-Reviewed Original ResearchConceptsPatient encountersNew patient evaluationsClinical workflowElectronic health record useOutpatient ophthalmology clinicHigh clinical volumeAcademic ophthalmology departmentsAcademic ophthalmology practicePaper-based documentationAge 43.9Postoperative visitRoutine followClinic hoursOphthalmology clinicOphthalmology departmentPatient evaluationOphthalmology practiceOutcome measurementsPatientsPatient careHours workRecord useOphthalmologistsClinical volumeTotal time
2017
A risk prediction score for acute kidney injury in the intensive care unit
Malhotra R, Kashani K, Macedo E, Kim J, Bouchard J, Wynn S, Li G, Ohno-Machado L, Mehta R. A risk prediction score for acute kidney injury in the intensive care unit. Nephrology Dialysis Transplantation 2017, 32: 814-822. PMID: 28402551, DOI: 10.1093/ndt/gfx026.Peer-Reviewed Original ResearchConceptsAcute kidney injuryIntensive care unitAcute risk factorsRisk score modelICU admissionKidney injuryCare unitValidation cohortKidney diseaseRisk factorsTest cohortTreatment of AKIAtherosclerotic coronary vascular diseaseMulticenter prospective cohort studyGlobal Outcomes criteriaChronic kidney diseaseHigh-risk patientsProspective cohort studyChronic liver diseaseCongestive heart failureTime of screeningCoronary vascular diseaseRisk prediction scoreEarly therapeutic interventionExternal validation cohort
2014
GAMUT: GPU accelerated microRNA analysis to uncover target genes through CUDA-miRanda
Wang S, Kim J, Jiang X, Brunner S, Ohno-Machado L. GAMUT: GPU accelerated microRNA analysis to uncover target genes through CUDA-miRanda. BMC Medical Genomics 2014, 7: s9. PMID: 25077821, PMCID: PMC4101446, DOI: 10.1186/1755-8794-7-s1-s9.Peer-Reviewed Original ResearchConceptsCompute Unified Device ArchitectureGraphics processing unitsHigh performance computeParallel computingNVIDIA Compute Unified Device ArchitectureUnified Device ArchitectureMultiple test datasetsGiga cell updatesTimes performance gainsSmith-Waterman algorithmGPU developersSW implementationSource codeExecution timeGHz CPUIntel XeonLong reference sequencesProcessing unitTarget identification algorithmCell updatesTest datasetProjects/Such large scalePerformance gainsBiomedical research community
2009
Is there an advantage in scoring early embryos on more than one day?
Racowsky C, Ohno-Machado L, Kim J, Biggers J. Is there an advantage in scoring early embryos on more than one day? Human Reproduction 2009, 24: 2104-2113. PMID: 19493872, PMCID: PMC2727402, DOI: 10.1093/humrep/dep198.Peer-Reviewed Original Research
2004
Genomic Analysis of Mouse Retinal Development
Blackshaw S, Harpavat S, Trimarchi J, Cai L, Huang H, Kuo W, Weber G, Lee K, Fraioli R, Cho S, Yung R, Asch E, Ohno-Machado L, Wong W, Cepko C. Genomic Analysis of Mouse Retinal Development. PLOS Biology 2004, 2: e247. PMID: 15226823, PMCID: PMC439783, DOI: 10.1371/journal.pbio.0020247.Peer-Reviewed Original ResearchMeSH KeywordsAnimalsBromodeoxyuridineCell LineageChromosome MappingCluster AnalysisComputational BiologyDatabases, GeneticExpressed Sequence TagsGene Expression RegulationGene Expression Regulation, DevelopmentalGene LibraryGenomeIn Situ HybridizationInterneuronsMiceMitosisMolecular Sequence DataNeurogliaOpen Reading FramesRetinaRNA, MessengerStem CellsTime FactorsConceptsMitotic progenitor cellsRetinal cell typesGene expressionCell typesExpression patternsRetinal developmentDevelopmental gene expression patternsGene expression patternsMajor retinal cell typesOpen reading frameProgenitor cellsMüller gliaPhotoreceptor-enriched genesGene expression profilesMouse retinal developmentMajor cell typesRetinal disease genesGenomic analysisMultiple retinal cell typesChromosomal intervalMolecular atlasMultiple transcriptsReading frameTaxonomic classificationDisease genes
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