2021
Privacy-protecting, reliable response data discovery using COVID-19 patient observations
Kim J, Neumann L, Paul P, Day M, Aratow M, Bell D, Doctor J, Hinske L, Jiang X, Kim K, Matheny M, Meeker D, Pletcher M, Schilling L, SooHoo S, Xu H, Zheng K, Ohno-Machado L, Anderson D, Anderson N, Balacha C, Bath T, Baxter S, Becker-Pennrich A, Bernstam E, Carter W, Chau N, Choi Y, Covington S, DuVall S, El-Kareh R, Florian R, Follett R, Geisler B, Ghigi A, Gottlieb A, Hu Z, Ir D, Knight T, Koola J, Kuo T, Lee N, Mansmann U, Mou Z, Murphy R, Neumann L, Nguyen N, Niedermayer S, Park E, Perkins A, Post K, Rieder C, Scherer C, Soares A, Soysal E, Tep B, Toy B, Wang B, Wu Z, Zhou Y, Zucker R. Privacy-protecting, reliable response data discovery using COVID-19 patient observations. Journal Of The American Medical Informatics Association 2021, 28: 1765-1776. PMID: 34051088, PMCID: PMC8194878, DOI: 10.1093/jamia/ocab054.Peer-Reviewed Original ResearchVERTIcal Grid lOgistic regression with Confidence Intervals (VERTIGO-CI).
Kim J, Li W, Bath T, Jiang X, Ohno-Machado L. VERTIcal Grid lOgistic regression with Confidence Intervals (VERTIGO-CI). AMIA Joint Summits On Translational Science Proceedings 2021, 2021: 355-364. PMID: 34457150, PMCID: PMC8378611.Peer-Reviewed Original ResearchConceptsDual spaceCovariance matrixVariance estimationSpace modelTest statisticKernel matrixLinear modelReal dataTolerable performanceNovel extensionDual-space modelPoint estimatesEquivalent accuracyCentralized versionStatisticsRegression modelsModelMatrixExtensionDual objectivesCentralized settingFederated LearningEstimationPrivacy-preserving mannerSpacePredictive Analytics for Glaucoma Using Data From the All of Us Research Program
Baxter S, Saseendrakumar B, Paul P, Kim J, Bonomi L, Kuo T, Loperena R, Ratsimbazafy F, Boerwinkle E, Cicek M, Clark C, Cohn E, Gebo K, Mayo K, Mockrin S, Schully S, Ramirez A, Ohno-Machado L, Investigators A. Predictive Analytics for Glaucoma Using Data From the All of Us Research Program. American Journal Of Ophthalmology 2021, 227: 74-86. PMID: 33497675, PMCID: PMC8184631, DOI: 10.1016/j.ajo.2021.01.008.Peer-Reviewed Original ResearchConceptsGlaucoma surgeryPrimary open-angle glaucomaOphthalmic researchSingle-center cohortElectronic health record dataMultivariable logistic regressionSingle-center dataOpen-angle glaucomaHealth record dataMean ageClaims dataUs Research ProgramLogistic regressionSurgeryRecord dataOphthalmic imagingCharacteristic curveExternal validationGlaucomaCohortAUCSingle-center model
2019
Patient Perspectives About Decisions to Share Medical Data and Biospecimens for Research
Kim J, Kim H, Bell E, Bath T, Paul P, Pham A, Jiang X, Zheng K, Ohno-Machado L. Patient Perspectives About Decisions to Share Medical Data and Biospecimens for Research. JAMA Network Open 2019, 2: e199550. PMID: 31433479, PMCID: PMC6707015, DOI: 10.1001/jamanetworkopen.2019.9550.Peer-Reviewed Original Research
2017
A Predictive Model for Extended Postanesthesia Care Unit Length of Stay in Outpatient Surgeries
Gabriel R, Waterman R, Kim J, Ohno-Machado L. A Predictive Model for Extended Postanesthesia Care Unit Length of Stay in Outpatient Surgeries. Anesthesia & Analgesia 2017, 124: 1529-1536. PMID: 28079580, DOI: 10.1213/ane.0000000000001827.Peer-Reviewed Original ResearchMeSH KeywordsAdolescentAdultAge FactorsAgedAged, 80 and overAmbulatory Surgical ProceduresAnesthesiaChildChild, PreschoolCritical CareFemaleForecastingHumansHypertensionInfantInfant, NewbornIntensive Care UnitsLength of StayLogistic ModelsMaleMiddle AgedModels, StatisticalObesity, MorbidPostoperative CareRisk FactorsROC CurveYoung AdultConceptsPACU lengthPostanesthesia care unit lengthPrimary anesthesia typePostanesthesia care unitHosmer-Lemeshow testLogistic regression modelsAnesthesia typeMorbid obesityCare unitHL testOutpatient surgeryOutpatient procedureSingle institutionHigher oddsNonsignificant P valuesStayPatientsSurgical specialtiesROC curveGood calibrationCharacteristic curveExcellent discriminationAUC valuesP-valueBackward elimination
2012
Selecting cases for whom additional tests can improve prognostication.
Jiang X, Kim J, Wu Y, Wang S, Ohno-Machado L. Selecting cases for whom additional tests can improve prognostication. AMIA Annual Symposium Proceedings 2012, 2012: 1260-8. PMID: 23304404, PMCID: PMC3540468.Peer-Reviewed Original ResearchGrid 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 ResearchConceptsIntegrity of communicationCentralized data sourcesTraditional LR modelCentral repositoryComputational costData sourcesData setsSame formatPatient dataComputationGenomic dataRare patternRelevant dataLR modelPrediction valueSetRepositoryPartial elementsFormatClassificationCommunicationModelDataPatient setPerformCalibrating predictive model estimates to support personalized medicine
Jiang X, Osl M, Kim J, Ohno-Machado L. Calibrating predictive model estimates to support personalized medicine. Journal Of The American Medical Informatics Association 2012, 19: 263-274. PMID: 21984587, PMCID: PMC3277613, DOI: 10.1136/amiajnl-2011-000291.Peer-Reviewed Original ResearchMeSH KeywordsConfidence IntervalsHumansLogistic ModelsMathematical ConceptsModels, TheoreticalPatient DischargePrecision MedicineConceptsReal-world medical classification problemsMedical classification problemsClassification problemPredictive model estimatesComputational complexityACP algorithmIsotonic regressionPredictive modelTerms of areaImportant performance measuresCalibration methodAdaptive techniqueCalculation of CIsAdaptive calibrationSquared errorIndividual predictionsPerformance measuresFit testInformationNew calibration methodCurrent methods
2011
Anomaly 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 ResearchConceptsSuspicious 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
2010
Positive predictive value of CT urography in the evaluation of upper tract urothelial cancer.
Sadow C, Wheeler S, Kim J, Ohno-Machado L, Silverman S. Positive predictive value of CT urography in the evaluation of upper tract urothelial cancer. American Journal Of Roentgenology 2010, 195: w337-43. PMID: 20966298, DOI: 10.2214/ajr.09.4147.Peer-Reviewed Original ResearchConceptsUpper tract urothelial cancerPositive predictive valueUrothelial cancerUrine cytologyCT urographyPredictive valueUrographic examinationUrothelial thickeningExact testUpper tract urothelial malignanciesMultivariate logistic regression analysisPositive urine cytologyRecords of patientsSignificant univariate predictorsLogistic regression analysisFisher's exact testT-testStudent's t-testEffect of ageFollowup imagingRetrospective reviewPathologic examinationUrothelial malignancyUnivariate predictorsMimic cancer