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 Research
2020
Privacy-preserving model learning on a blockchain network-of-networks
Kuo T, Kim J, Gabriel R. Privacy-preserving model learning on a blockchain network-of-networks. Journal Of The American Medical Informatics Association 2020, 27: 343-354. PMID: 31943009, PMCID: PMC7025358, DOI: 10.1093/jamia/ocz214.Peer-Reviewed Original ResearchConceptsNetwork topologyExecution timeArt methodsPredictive correctnessPrivacy-preserving learningPrivacy-preserving methodsPrivacy-preserving modelSmall training datasetBlockchain networkBlockchain platformBlockchain technologyPrivacy concernsModel learningComplex dataLearning iterationsLearning methodsTraining datasetConsensus algorithmGeneralizable predictive modelsCorrectness resultsModel disseminationHierarchical networkSmall dataHierarchical approachRecord model
2012
Grid 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 setPerformiDASH: integrating data for analysis, anonymization, and sharing
Ohno-Machado L, Bafna V, Boxwala A, Chapman B, Chapman W, Chaudhuri K, Day M, Farcas C, Heintzman N, Jiang X, Kim H, Kim J, Matheny M, Resnic F, Vinterbo S, team A. iDASH: integrating data for analysis, anonymization, and sharing. Journal Of The American Medical Informatics Association 2012, 19: 196-201. PMID: 22081224, PMCID: PMC3277627, DOI: 10.1136/amiajnl-2011-000538.Commentaries, Editorials and LettersConceptsHigh-performance computing environmentPrivacy-preserving mannerCollaborative tool developmentData-sharing capabilitiesData ownersComputing environmentData consumersBiomedical computingHealth Insurance PortabilityTechnology researchTool developmentAccountability ActBiological projectsBiological dataInsurance PortabilityAnonymizationComputingPortabilityBehavioral researchersAlgorithmSoftwareCloudNew National CenterDataCapability