2020
iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching
Kuo T, Jiang X, Tang H, Wang X, Bath T, Bu D, Wang L, Harmanci A, Zhang S, Zhi D, Sofia H, Ohno-Machado L. iDASH secure genome analysis competition 2018: blockchain genomic data access logging, homomorphic encryption on GWAS, and DNA segment searching. BMC Medical Genomics 2020, 13: 98. PMID: 32693816, PMCID: PMC7372776, DOI: 10.1186/s12920-020-0715-0.Peer-Reviewed Original ResearchEXpectation 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/disadvantagesPrivacy-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
2018
Identifying and characterizing highly similar notes in big clinical note datasets
Gabriel R, Kuo T, McAuley J, Hsu C. Identifying and characterizing highly similar notes in big clinical note datasets. Journal Of Biomedical Informatics 2018, 82: 63-69. PMID: 29679685, DOI: 10.1016/j.jbi.2018.04.009.Peer-Reviewed Original ResearchConceptsClinical note datasetsDe-duplication algorithmMIMIC-III datasetElectronic health recordsJaccard similarityDe-duplicationLocality sensitive hashingMIMIC-IIINear-duplicatesScalable algorithmMeasure similarityAccurate statistical modelsSources of duplicationClustering methodDatasetAlgorithmApproximation algorithmHealth recordsDisjoint setsInstitutional datasetComparison of notesPairs of notesHashPairwise comparisonsPairwise