2022
Inclusion of social determinants of health improves sepsis readmission prediction models
Amrollahi F, Shashikumar S, Meier A, Ohno-Machado L, Nemati S, Wardi G. Inclusion of social determinants of health improves sepsis readmission prediction models. Journal Of The American Medical Informatics Association 2022, 29: 1263-1270. PMID: 35511233, PMCID: PMC9196687, DOI: 10.1093/jamia/ocac060.Peer-Reviewed Original ResearchMeSH KeywordsHumansLogistic ModelsPatient ReadmissionRetrospective StudiesRisk FactorsSepsisSocial Determinants of HealthConceptsUnplanned readmissionSepsis patientsReadmission modelsClinical/laboratory featuresSocial determinantsUnplanned hospital readmissionHigh-risk patientsObjective clinical dataLow predictive valueReadmission prediction modelsSepsis readmissionsLaboratory featuresSepsis casesHospital readmissionPredictive factorsClinical dataReadmissionHigh riskPredictive valueSDH factorsMedical carePatientsDemographic featuresLarger studyProgram cohort
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
2020
EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning
Kuo T, Gabriel R, Cidambi K, Ohno-Machado L. EXpectation Propagation LOgistic REgRession on permissioned blockCHAIN (ExplorerChain): decentralized online healthcare/genomics predictive model learning. Journal Of The American Medical Informatics Association 2020, 27: 747-756. PMID: 32364235, PMCID: PMC7309256, DOI: 10.1093/jamia/ocaa023.Peer-Reviewed Original ResearchConceptsBlockchain technologyCentral serverServer-based methodBenefits of blockchainData protection policiesCentralized serverArtificial intelligenceModel learningDecentralized approachSmall datasetsBlockchainServerComputation strategySingle pointGeneralizable modelCost of efficiencyGenomic datasetsDatasetDistributed modelTechnologyGenomic dataMultiple institutionsDiscrimination powerIntelligencePotential advantages/disadvantagesA tutorial on calibration measurements and calibration models for clinical prediction models
Huang Y, Li W, Macheret F, Gabriel R, Ohno-Machado L. A tutorial on calibration measurements and calibration models for clinical prediction models. Journal Of The American Medical Informatics Association 2020, 27: 621-633. PMID: 32106284, PMCID: PMC7075534, DOI: 10.1093/jamia/ocz228.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus Statements
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
2016
Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE)
Shi H, Jiang C, Dai W, Jiang X, Tang Y, Ohno-Machado L, Wang S. Secure Multi-pArty Computation Grid LOgistic REgression (SMAC-GLORE). BMC Medical Informatics And Decision Making 2016, 16: 89. PMID: 27454168, PMCID: PMC4959358, DOI: 10.1186/s12911-016-0316-1.Peer-Reviewed Original ResearchConceptsData sharingPatient privacySecure multi-party computationModel learning phaseMulti-party computationBiomedical data sharingInformation leakageModel learningIntermediary informationInformation exchangeSecondary usePrivacyBig concernPractical solutionLogistic regression frameworkExperimental resultsSharingRegression frameworkFrameworkMultiple institutionsPrevious workComputationLearningBiomedical researchInformation
2015
VERTIcal Grid lOgistic regression (VERTIGO)
Li Y, Jiang X, Wang S, Xiong H, Ohno-Machado L. VERTIcal Grid lOgistic regression (VERTIGO). Journal Of The American Medical Informatics Association 2015, 23: 570-579. PMID: 26554428, PMCID: PMC4901373, DOI: 10.1093/jamia/ocv146.Peer-Reviewed Original ResearchConceptsFederated data analysisReal-world medical classification problemsMedical classification problemsLogistic regression algorithmAccurate global modelData setsReal data setsClassification problemExchange of informationLR problemTime complexityComputational complexityExpensive operationRegression algorithmComputational costData analysisAlgorithmDual optimizationTechnical challengesLarge amountComplexityPatient recordsLR modelNovel techniqueHessian matrixGrid multi-category response logistic models
Wu Y, Jiang X, Wang S, Jiang W, Li P, Ohno-Machado L. Grid multi-category response logistic models. BMC Medical Informatics And Decision Making 2015, 15: 10. PMID: 25886151, PMCID: PMC4342889, DOI: 10.1186/s12911-015-0133-y.Peer-Reviewed Original ResearchMeSH KeywordsClinical Decision-MakingConfidentialityHumansInformation DisseminationLogistic ModelsModels, StatisticalConceptsGrid modelLikelihood estimation problemClassification performance evaluationReal data setsGrid computingEstimation problemTypes of modelsGrid computationGrid methodPrivacyResponse modelCentralized modelMulti-center dataSuch decompositionsFit assessmentFitting methodLinear modelPerformance evaluationModel constructionData setsModel assumptionsIndividual observationsPractical solutionComputationResultsSimulation results
2014
Differentially private distributed logistic regression using private and public data
Ji Z, Jiang X, Wang S, Xiong L, Ohno-Machado L. Differentially private distributed logistic regression using private and public data. BMC Medical Genomics 2014, 7: s14. PMID: 25079786, PMCID: PMC4101668, DOI: 10.1186/1755-8794-7-s1-s14.Peer-Reviewed Original ResearchMeSH KeywordsAccess to InformationAlgorithmsBreast NeoplasmsData MiningHumansLogistic ModelsMedical InformaticsPatient DischargePrivacyConceptsPrivate dataDifferential privacyPublic datasetsPublic dataRigorous privacy guaranteeData privacy researchPrivate data setsData mining modelsData setsProvable privacyPrivacy guaranteesMining modelPrivacy researchDifferent data setsArt frameworksMedical informaticsPrivacyAmount of noisePrivate methodsAuxiliary informationBetter utilityNew algorithmUpdate stepAvailable public dataAlgorithm
2013
WebGLORE: a Web service for Grid LOgistic REgression
Jiang W, Li P, Wang S, Wu Y, Xue M, Ohno-Machado L, Jiang X. WebGLORE: a Web service for Grid LOgistic REgression. Bioinformatics 2013, 29: 3238-3240. PMID: 24072732, PMCID: PMC3842761, DOI: 10.1093/bioinformatics/btt559.Peer-Reviewed Original ResearchConceptsWeb servicesHypertext Transfer Protocol SecurePrivacy-preserving constructionFree Software FoundationGNU General Public LicenseUse web serviceFree web serviceGeneral Public LicenseDistributed datasetsTrusted serverProtocol SecureSoftware FoundationPublic LicensePHP technologyInformation exchangeBiomedical researchersLocal statisticsServicesServletsServerSecureAjaxGlobal logistic regression modelDatasetGlobal modelEXpectation Propagation LOgistic REgRession (EXPLORER): Distributed privacy-preserving online model learning
Wang S, Jiang X, Wu Y, Cui L, Cheng S, Ohno-Machado L. EXpectation Propagation LOgistic REgRession (EXPLORER): Distributed privacy-preserving online model learning. Journal Of Biomedical Informatics 2013, 46: 480-496. PMID: 23562651, PMCID: PMC3676314, DOI: 10.1016/j.jbi.2013.03.008.Peer-Reviewed Original ResearchConceptsHigh-level guaranteesOnline model learningSensitive informationModel learningEntire dataOnline learningAbsence of participantsMore flexibilitySame performanceExperimental resultsLearningCommunicationServerInformationGuaranteesModel updatingPosterior distributionServicesClientsUpdatingFrameworkFlexibilityModelPerformance
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 ResearchPreserving Institutional Privacy in Distributed binary Logistic Regression.
Wu Y, Jiang X, Ohno-Machado L. Preserving Institutional Privacy in Distributed binary Logistic Regression. AMIA Annual Symposium Proceedings 2012, 2012: 1450-8. PMID: 23304425, PMCID: PMC3540539.Peer-Reviewed Original ResearchAn improved model for predicting postoperative nausea and vomiting in ambulatory surgery patients using physician-modifiable risk factors
Sarin P, Urman R, Ohno-Machado L. An improved model for predicting postoperative nausea and vomiting in ambulatory surgery patients using physician-modifiable risk factors. Journal Of The American Medical Informatics Association 2012, 19: 995-1002. PMID: 22582204, PMCID: PMC3534465, DOI: 10.1136/amiajnl-2012-000872.Peer-Reviewed Original ResearchConceptsAmbulatory surgery dataExperimental modelNon-modifiable patient characteristicsApfel risk scoreAmbulatory surgery patientsGood calibrationLogistic regression modelsAmbulatory surgery casesPONV prophylaxisPostoperative nauseaFrequent complicationPatient characteristicsSurgery patientsAnesthetic techniqueAmbulatory surgeryPatient riskRisk factorsSurgery casesAnaesthetic practiceRisk scoreAcademic centersSurgery dataPractice improvementNauseaVomitingGrid 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
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 Research