2024
A machine learning framework to adjust for learning effects in medical device safety evaluation
Koola J, Ramesh K, Mao J, Ahn M, Davis S, Govindarajulu U, Perkins A, Westerman D, Ssemaganda H, Speroff T, Ohno-Machado L, Ramsay C, Sedrakyan A, Resnic F, Matheny M. A machine learning framework to adjust for learning effects in medical device safety evaluation. Journal Of The American Medical Informatics Association 2024, ocae273. PMID: 39471493, DOI: 10.1093/jamia/ocae273.Peer-Reviewed Original ResearchMachine learning frameworkSynthetic datasetsLearning frameworkMachine learningCapacity of MLLearning effectFeature correlationDepartment of Veterans AffairsSynthetic dataData generationAbsence of learning effectsTraditional statistical methodsML methodsSuperior performanceDatasetSafety signal detectionSignal detectionDevice signalsVeterans AffairsTime-varying covariatesLearningMachinePhysician experienceLimitations of traditional statistical methodsMedical device post-market surveillance
2023
Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance
Davis S, Ssemaganda H, Koola J, Mao J, Westerman D, Speroff T, Govindarajulu U, Ramsay C, Sedrakyan A, Ohno-Machado L, Resnic F, Matheny M. Simulating complex patient populations with hierarchical learning effects to support methods development for post-market surveillance. BMC Medical Research Methodology 2023, 23: 89. PMID: 37041457, PMCID: PMC10088292, DOI: 10.1186/s12874-023-01913-9.Peer-Reviewed Original ResearchConceptsSynthetic datasetsData characteristicsFeature distributionGround truthMIMIC-III dataReal-world dataData generation processComplex simulation studiesData relationshipsUser definitionSmall datasetsSimulation requirementsCorrelated featuresWorld dataCustomizable optionsReal-world complexitySynthetic patientsNew algorithmDatasetGeneration processLearningAlgorithmData simulation techniquesLearning effectGeneralizable framework
2019
Protecting patient privacy in survival analyses
Bonomi L, Jiang X, Ohno-Machado L. Protecting patient privacy in survival analyses. Journal Of The American Medical Informatics Association 2019, 27: 366-375. PMID: 31750926, PMCID: PMC7025359, DOI: 10.1093/jamia/ocz195.Peer-Reviewed Original ResearchConceptsPrivacy protectionPrivacy risksHealthcare applicationsPatient privacyPrivacy protection methodProvable privacy protectionStrong privacy protectionPerson of interestKnowledgeable adversaryDifferential privacySynthetic datasetsFormal modelEpidemiology datasetPrivacyNonparametric survival modelFuture research directionsAdversaryResearch directionsDatasetBiomedical research applicationsFrameworkFrequent sharingResearch applicationsApplicationsSharing