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
Chapter 7 Data-driven approaches to generating knowledge: Machine learning, artificial intelligence, and predictive modeling
Matheny M, Ohno-Machado L, Davis S, Nemati S. Chapter 7 Data-driven approaches to generating knowledge: Machine learning, artificial intelligence, and predictive modeling. 2023, 217-255. DOI: 10.1016/b978-0-323-91200-6.00031-0.Peer-Reviewed Original Research
2013
Detecting inappropriate access to electronic health records using collaborative filtering
Menon A, Jiang X, Kim J, Vaidya J, Ohno-Machado L. Detecting inappropriate access to electronic health records using collaborative filtering. Machine Learning 2013, 95: 87-101. PMID: 24683293, PMCID: PMC3967851, DOI: 10.1007/s10994-013-5376-1.Peer-Reviewed Original ResearchElectronic health recordsCollaborative filteringInappropriate accessHealth recordsSuspicious accessPrivacy policiesAccess patternsMachine learningManual auditingSecurity expertsLatent featuresAccess dataRecord accessHistorical dataSecurityFilteringUnrestricted accessFuture violationsAccessAudit processSVMUsersDatasetLearningAuditing
2011
Smooth isotonic regression: a new method to calibrate predictive models.
Jiang X, Osl M, Kim J, Ohno-Machado L. Smooth isotonic regression: a new method to calibrate predictive models. AMIA Joint Summits On Translational Science Proceedings 2011, 2011: 16-20. PMID: 22211175, PMCID: PMC3248752.Peer-Reviewed Original ResearchBiomedical data setsSupervised learning modelGood generalization abilityMachine learningPredictive modelGeneralization abilityProbabilistic outputsLearning modelData setsIsotonic regression methodNovel methodNon-parametric approachReliability diagramsProbability estimatesRegression methodNew methodLearning