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 ResearchPredictive 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
2018
Genomics and electronic health record systems
Ohno-Machado L, Kim J, Gabriel R, Kuo G, Hogarth M. Genomics and electronic health record systems. Human Molecular Genetics 2018, 27: r48-r55. PMID: 29741693, PMCID: PMC5946823, DOI: 10.1093/hmg/ddy104.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsElectronic health recordsHigh-level viewElectronic health record systemsHealth record systemsNext generation systemsEHR systemsSeamless fashionOpen issuesMyriad of approachesHealth recordsRecord systemSpecific solutionsEnd goalSources of informationAnalysis of genomesInformationCustomizationSystemGeneration systemVisionDegrees of successFunctionality
2016
iCONCUR: informed consent for clinical data and bio-sample use for research
Kim H, Bell E, Kim J, Sitapati A, Ramsdell J, Farcas C, Friedman D, Feupe S, Ohno-Machado L. iCONCUR: informed consent for clinical data and bio-sample use for research. Journal Of The American Medical Informatics Association 2016, 24: 380-387. PMID: 27589942, PMCID: PMC5391727, DOI: 10.1093/jamia/ocw115.Peer-Reviewed Original ResearchMeSH KeywordsBiomedical ResearchElectronic Health RecordsFeasibility StudiesFemaleHumansInformation DisseminationInformed ConsentMalePatient PreferenceSocioeconomic FactorsConceptsPatient preferencesClinical dataHuman immunodeficiency virus clinicInternal medicine clinicElectronic health record dataHealth record dataAcademic medical centerElectronic health recordsDe-identified dataOutpatient clinicMedicine clinicFamily historyMedical CenterInformed Consent ToolClinical settingClinicHealth recordsRecord dataInformed consent systemPatientsConsent toolRecipientsConsentE-consentParticipants
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 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