2020
The Data Tags Suite (DATS) model for discovering data access and use requirements
Alter G, Gonzalez-Beltran A, Ohno-Machado L, Rocca-Serra P. The Data Tags Suite (DATS) model for discovering data access and use requirements. GigaScience 2020, 9: giz165. PMID: 32031623, PMCID: PMC7006671, DOI: 10.1093/gigascience/giz165.Peer-Reviewed Original ResearchConceptsData accessData discovery toolsPrivacy of subjectsData use agreementsConfidential dataMetadata itemsData reuseMetadata schemaAutomated systemDiscovery toolTechnical systemsStandard wayUse agreementsAccessPrivacyMetadataSchemaUse requirementsReuseResearchersResearch dataSystemRequirementsInformationData
2018
Finding relevant biomedical datasets: the UC San Diego solution for the bioCADDIE Retrieval Challenge
Wei W, Ji Z, He Y, Zhang K, Ha Y, Li Q, Ohno-Machado L. Finding relevant biomedical datasets: the UC San Diego solution for the bioCADDIE Retrieval Challenge. Database 2018, 2018: bay017. PMID: 29688374, PMCID: PMC5861401, DOI: 10.1093/database/bay017.Peer-Reviewed Original Research
2017
Blockchain distributed ledger technologies for biomedical and health care applications
Kuo T, Kim H, Ohno-Machado L. Blockchain distributed ledger technologies for biomedical and health care applications. Journal Of The American Medical Informatics Association 2017, 24: 1211-1220. PMID: 29016974, PMCID: PMC6080687, DOI: 10.1093/jamia/ocx068.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsHealth care domainHealth care applicationsBlockchain technologyLedger technologyCare domainFeatures of blockchainBenefits of blockchainCare applicationsFamous BitcoinInformatics researchersBlockchainTechnologyApplicationsCovered topicsBitcoinDomainLatest applicationsPotential challengesDatabaseAdvancing healthcare and biomedical research via new data-driven approaches
Ohno-Machado L. Advancing healthcare and biomedical research via new data-driven approaches. Journal Of The American Medical Informatics Association 2017, 24: 471-471. PMID: 28403381, PMCID: PMC7651977, DOI: 10.1093/jamia/ocx036.Commentaries, Editorials and Letters
2016
Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States
Wang S, Jiang X, Singh S, Marmor R, Bonomi L, Fox D, Dow M, Ohno‐Machado L. Genome privacy: challenges, technical approaches to mitigate risk, and ethical considerations in the United States. Annals Of The New York Academy Of Sciences 2016, 1387: 73-83. PMID: 27681358, PMCID: PMC5266631, DOI: 10.1111/nyas.13259.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsData privacySensitive individual informationComputer science communityReal-world problemsUnauthorized partiesHuman genomic dataPrivacy breachesData accessData sharingData accessibilityConfidentiality protectionGenomic dataSpectrum of techniquesIndividual informationPrivacyScience communityPhenotype informationTechnical approachPotential solutionsCurrent common practiceBiomedical researchResearch purposesConfidentialityInformationSharingBuilding 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 ResearchConceptsDiagnosis 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
2015
Mining electronic health record data: finding the gold nuggets
Ohno-Machado L, Editor-in-Chief. Mining electronic health record data: finding the gold nuggets. Journal Of The American Medical Informatics Association 2015, 22: 937-937. PMID: 26330468, DOI: 10.1093/jamia/ocv119.Commentaries, Editorials and Letters
2014
“Big Data” and the Electronic Health Record
Ross M, Wei W, Ohno-Machado L. “Big Data” and the Electronic Health Record. Yearbook Of Medical Informatics 2014, 23: 97-104. PMID: 25123728, PMCID: PMC4287068, DOI: 10.15265/iy-2014-0003.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsBig dataEHR systemsElectronic health record systemsHealth record systemsData miningElectronic health recordsData applicationsActionable knowledgeMassive numberAdditional keywordsNew keywordsSecondary useInformatics professionalsHealth recordsRecord systemKeywordsLarge amountPrivacyNext stepMiningSecurityEHRSystemImplementationDataBig Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients
Bates D, Saria S, Ohno-Machado L, Shah A, Escobar G. Big Data In Health Care: Using Analytics To Identify And Manage High-Risk And High-Cost Patients. Health Affairs 2014, 33: 1123-1131. PMID: 25006137, DOI: 10.1377/hlthaff.2014.0041.Commentaries, Editorials and LettersConceptsBig dataClinical analyticsPrivacy concernsUse casesElectronic health recordsAnalyticsTypes of dataHealth recordsTypes of insightsNecessary analysisSupport of researchHigh-cost patientsUnprecedented opportunityMonitoring devicesCostHealth careAlgorithmMultiple organ systemsRapid progressInfrastructureUS health care systemHealth care systemSystemAdverse eventsClinical dataDifferentially 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 ResearchConceptsPrivate 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 dataAlgorithmNIH's Big Data to Knowledge initiative and the advancement of biomedical informatics
Ohno-Machado L. NIH's Big Data to Knowledge initiative and the advancement of biomedical informatics. Journal Of The American Medical Informatics Association 2014, 21: 193-193. PMID: 24509598, PMCID: PMC3932475, DOI: 10.1136/amiajnl-2014-002666.Commentaries, Editorials and Letters
2012
Privacy-preserving heterogeneous health data sharing
Mohammed N, Jiang X, Chen R, Fung B, Ohno-Machado L. Privacy-preserving heterogeneous health data sharing. Journal Of The American Medical Informatics Association 2012, 20: 462-469. PMID: 23242630, PMCID: PMC3628047, DOI: 10.1136/amiajnl-2012-001027.Peer-Reviewed Original ResearchMeSH KeywordsAlgorithmsConfidentialityData MiningDatabases, FactualFemaleHumansInformation DisseminationMalePrivacyConceptsSet-valued dataDifferential privacyNoise additionPrivacy-preserving mannerAdversary's background knowledgeStrong privacy guaranteesBackground knowledgeHealth data sharingPrivacy modelPrivacy guaranteesSensitive dataData sharingHealthcare dataPrivate mannerAlgorithm designPrivacyRaw dataSynthetic dataAlgorithmHealth dataProbabilistic wayDiscriminative analysisExperimental resultsUseful informationClassification analysis