2017
A risk prediction score for acute kidney injury in the intensive care unit
Malhotra R, Kashani K, Macedo E, Kim J, Bouchard J, Wynn S, Li G, Ohno-Machado L, Mehta R. A risk prediction score for acute kidney injury in the intensive care unit. Nephrology Dialysis Transplantation 2017, 32: 814-822. PMID: 28402551, DOI: 10.1093/ndt/gfx026.Peer-Reviewed Original ResearchConceptsAcute kidney injuryIntensive care unitAcute risk factorsRisk score modelICU admissionKidney injuryCare unitValidation cohortKidney diseaseRisk factorsTest cohortTreatment of AKIAtherosclerotic coronary vascular diseaseMulticenter prospective cohort studyGlobal Outcomes criteriaChronic kidney diseaseHigh-risk patientsProspective cohort studyChronic liver diseaseCongestive heart failureTime of screeningCoronary vascular diseaseRisk prediction scoreEarly therapeutic interventionExternal validation cohort
2012
iDASH: 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
2011
Using statistical and machine learning to help institutions detect suspicious access to electronic health records
Boxwala A, Kim J, Grillo J, Ohno-Machado L. Using statistical and machine learning to help institutions detect suspicious access to electronic health records. Journal Of The American Medical Informatics Association 2011, 18: 498-505. PMID: 21672912, PMCID: PMC3128412, DOI: 10.1136/amiajnl-2011-000217.Peer-Reviewed Original ResearchConceptsSuspicious accessMachine-learning methodsPrivacy officersMachine learning techniquesVector machine modelAccess logsElectronic health recordsBaseline methodsAccess dataCross-validation setGold standard setSVM modelWhole data setMachine modelBaseline modelOrganizational dataHealth recordsData setsSVM
2010
Electronic laboratory system reduces errors in National Tuberculosis Program: a cluster randomized controlled trial.
Blaya J, Shin S, Yale G, Suarez C, Asencios L, Contreras C, Rodriguez P, Kim J, Cegielski P, Fraser H. Electronic laboratory system reduces errors in National Tuberculosis Program: a cluster randomized controlled trial. The International Journal Of Tuberculosis And Lung Disease 2010, 14: 1009-15. PMID: 20626946, PMCID: PMC8324019.Peer-Reviewed Original ResearchConceptsControl health centersHealth centersE-ChasquiIntervention health centersNational Tuberculosis ProgrammeLaboratory information systemDrug susceptibility testTuberculosis careTuberculosis ProgrammeSusceptibility testsContinuous quality improvementBaseline dataMonthsInterventionFurther dataTrialsLaboratory resultsElectronic laboratory systemError reportsClinical usersSame monthWeeksCare