2022
Concordance of SARS-CoV-2 Antibody Results during a Period of Low Prevalence
Miller C, Althoff K, Schlueter D, Anton-Culver H, Chen Q, Garbett S, Ratsimbazafy F, Thomsen I, Karlson E, Cicek M, Pinto L, Malin B, Ohno-Machado L, Williams C, Goldstein D, Kouame A, Ramirez A, Gebo K, Schully S. Concordance of SARS-CoV-2 Antibody Results during a Period of Low Prevalence. MSphere 2022, 7: e00257-22. PMID: 36173112, PMCID: PMC9599436, DOI: 10.1128/msphere.00257-22.Peer-Reviewed Original ResearchMeSH KeywordsAntibodies, ViralCOVID-19HumansImmunoglobulin GPopulation HealthPrevalenceSARS-CoV-2Sensitivity and SpecificitySeroepidemiologic StudiesConceptsSARS-CoV-2 antibody concentrationsLow prevalenceVaccine availabilitySARS-CoV-2 enzyme-linked immunosorbent assayFalse positivityLow SARS-CoV-2 prevalenceSARS-CoV-2 IgG assaysSARS-CoV-2 IgG testSARS-CoV-2 IgG antibodiesSevere acute respiratory syndrome coronavirus 2Coronavirus disease 2019 prevalenceAcute respiratory syndrome coronavirus 2Antibody concentrationsSARS-CoV-2 testDifferent antigensRespiratory syndrome coronavirus 2SARS-CoV-2 prevalenceSyndrome coronavirus 2SARS-CoV-2 pandemicConcordant positive resultsConcordant negative resultsEnzyme-linked immunosorbent assayPositive resultsFuture pandemic preparednessConcordance of results
2021
Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in All of Us Research Program Participants, 2 January to 18 March 2020
Althoff K, Schlueter D, Anton-Culver H, Cherry J, Denny J, Thomsen I, Karlson E, Havers F, Cicek M, Thibodeau S, Pinto L, Lowy D, Malin B, Ohno-Machado L, Williams C, Goldstein D, Kouame A, Ramirez A, Roman A, Sharpless N, Gebo K, Schully S. Antibodies to Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) in All of Us Research Program Participants, 2 January to 18 March 2020. Clinical Infectious Diseases 2021, 74: 584-590. PMID: 34128970, PMCID: PMC8384413, DOI: 10.1093/cid/ciab519.Peer-Reviewed Original ResearchMeSH KeywordsAntibodies, ViralCOVID-19Enzyme-Linked Immunosorbent AssayHumansImmunoglobulin GPopulation HealthSARS-CoV-2Sensitivity and SpecificityConceptsEnzyme-linked immunosorbent assayBlood specimensSARS-CoV-2 enzyme-linked immunosorbent assaySARS-CoV-2 immunoglobulin G (IgG) antibodiesSARS-CoV-2 antibodiesIgG enzyme-linked immunosorbent assayConfidence intervalsImmunoglobulin G antibodiesSARS-CoV-2Sequential testing algorithmEUROIMMUN assaysSymptomatic patientsTesting algorithmStudy visitG antibodiesCommunity transmissionTravel historyStudy participantsUS epidemicEarly weeksImmunosorbent assayTesting capacityAntibodiesWeeksUS states
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
2016
Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes
Doan S, Maehara C, Chaparro J, Lu S, Liu R, Graham A, Berry E, Hsu C, Kanegaye J, Lloyd D, Ohno‐Machado L, Burns J, Tremoulet A, Group T. Building a Natural Language Processing Tool to Identify Patients With High Clinical Suspicion for Kawasaki Disease from Emergency Department Notes. Academic Emergency Medicine 2016, 23: 628-636. PMID: 26826020, PMCID: PMC5031359, DOI: 10.1111/acem.12925.Peer-Reviewed Original ResearchMeSH KeywordsChildData MiningElectronic Health RecordsEmergency Service, HospitalHumansMucocutaneous Lymph Node SyndromeNatural Language ProcessingSensitivity and SpecificityConceptsDiagnosis of KDKawasaki diseaseED notesHigh suspicionPediatric ED patientsSerious cardiac complicationsHigh clinical suspicionEmergency department patientsManual chart reviewCardiac complicationsChart reviewClinical suspicionFebrile illnessDepartment patientsED patientsElectronic health record systemsEmergency departmentClinical signsDiagnostic criteriaHealth record systemsPatientsClinical termsSuspicionDiagnosisRecord system
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
2011
Improving predictions in imbalanced data using Pairwise Expanded Logistic Regression.
Jiang X, El-Kareh R, Ohno-Machado L. Improving predictions in imbalanced data using Pairwise Expanded Logistic Regression. AMIA Annual Symposium Proceedings 2011, 2011: 625-34. PMID: 22195118, PMCID: PMC3243279.Peer-Reviewed Original ResearchArtificial IntelligenceDiseaseHumansLogistic ModelsMathematical ConceptsROC CurveSensitivity and SpecificityAnomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs.
Kim J, Grillo J, Boxwala A, Jiang X, Mandelbaum R, Patel B, Mikels D, Vinterbo S, Ohno-Machado L. Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs. AMIA Annual Symposium Proceedings 2011, 2011: 723-31. PMID: 22195129, PMCID: PMC3243249.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceComputer SecurityElectronic Health RecordsHumansLogistic ModelsPrivacySensitivity and SpecificityConceptsSuspicious accessAccess recordsRule-based techniquesMachine learning methodsConstruction of classifiersAnomaly detectionInformative instancesLearning methodsSymbolic clusteringClassifier performanceSignature detectionIndependent test setInappropriate accessTest setEHRFiltering methodIntegrated filtering strategyFiltering strategyClassifierFilteringNegative rateFalse negative rateAccessDetectionClusteringUsing statistical and machine learning to help institutions detect suspicious access to electronic health records
Boxwala A, Kim J, Grillo J, Ohno-Machado L. Using statistical and machine learning to help institutions detect suspicious access to electronic health records. Journal Of The American Medical Informatics Association 2011, 18: 498-505. PMID: 21672912, PMCID: PMC3128412, DOI: 10.1136/amiajnl-2011-000217.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceComputer SecurityElectronic Health RecordsHumansLogistic ModelsManagement AuditPilot ProjectsSensitivity and SpecificitySoftware ValidationUnited StatesConceptsSuspicious accessMachine-learning methodsPrivacy officersMachine learning techniquesVector machine modelAccess logsElectronic health recordsBaseline methodsAccess dataCross-validation setGold standard setSVM modelWhole data setMachine modelBaseline modelOrganizational dataHealth recordsData setsSVM
2007
Effects of SVM parameter optimization on discrimination and calibration for post-procedural PCI mortality
Matheny M, Resnic F, Arora N, Ohno-Machado L. Effects of SVM parameter optimization on discrimination and calibration for post-procedural PCI mortality. Journal Of Biomedical Informatics 2007, 40: 688-697. PMID: 17600771, PMCID: PMC2170520, DOI: 10.1016/j.jbi.2007.05.008.Peer-Reviewed Original ResearchConceptsSupport vector machineRadial Basis Kernel Support Vector MachineKernel support vector machineCross-entropy errorSVM parameter optimizationUnseen test dataSVM kernel typesTraining dataVector machineEvolutionary algorithmGrid searchMean squared errorKernel typeMachineOptimization methodPrediction modelNumber of methodsParameter optimizationTest dataMedical applicationsOptimization parametersMortality prediction modelAlgorithmBest modelApplications
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
2002
Analysis of matched mRNA measurements from two different microarray technologies
Kuo W, Jenssen T, Butte A, Ohno-Machado L, Kohane I. Analysis of matched mRNA measurements from two different microarray technologies. Bioinformatics 2002, 18: 405-412. PMID: 11934739, DOI: 10.1093/bioinformatics/18.3.405.Peer-Reviewed Original Research
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 ResearchMeSH KeywordsDisease-Free SurvivalHeart FailureHumansNeural Networks, ComputerPatient ReadmissionPrognosisRisk AssessmentSensitivity and SpecificityConceptsNeural 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
1998
Comparison 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
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
Hierarchical 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 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