2022
An Artificial Intelligence-guided signature reveals the shared host immune response in MIS-C and Kawasaki disease
Ghosh P, Katkar G, Shimizu C, Kim J, Khandelwal S, Tremoulet A, Kanegaye J, Bocchini J, Das S, Burns J, Sahoo D. An Artificial Intelligence-guided signature reveals the shared host immune response in MIS-C and Kawasaki disease. Nature Communications 2022, 13: 2687. PMID: 35577777, PMCID: PMC9110726, DOI: 10.1038/s41467-022-30357-w.Peer-Reviewed Original ResearchConceptsKawasaki diseaseHost immune responseLaboratory parametersImmune responseSARS-CoV-2 infectionInflammatory syndromeCytokine stormSerum cytokinesClinical featuresCytokine pathwaysCardiac phenotypeSyndromeGene signaturePediatric syndromesViral pandemicHeart tissueCOVID-19COVID-19 pandemicProximal pathwayDiseaseSeverityImmunopathogenesisPandemicCytokinesIllness
2011
A multi-layered framework for disseminating knowledge for computer-based decision support
Boxwala A, Rocha B, Maviglia S, Kashyap V, Meltzer S, Kim J, Tsurikova R, Wright A, Paterno M, Fairbanks A, Middleton B. A multi-layered framework for disseminating knowledge for computer-based decision support. Journal Of The American Medical Informatics Association 2011, 18: i132-i139. PMID: 22052898, PMCID: PMC3241169, DOI: 10.1136/amiajnl-2011-000334.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceDecision Making, Computer-AssistedDecision Support Systems, ClinicalPractice Guidelines as TopicSoftware DesignConceptsCDS systemsComputer-based decision supportPatient data representationKnowledge representation frameworkMulti-layered frameworkData representationDecision support toolStructured knowledgeClinical decision support toolCDS servicesText recommendationDecision supportRepresentation frameworkSupport toolPreliminary evaluationFrameworkDifferent clinical sitesGuideline knowledgeWorkflowSystemImplementationServicesImplementabilityKnowledgeRepresentationAnomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs.
Kim J, Grillo J, Boxwala A, Jiang X, Mandelbaum R, Patel B, Mikels D, Vinterbo S, Ohno-Machado L. Anomaly and signature filtering improve classifier performance for detection of suspicious access to EHRs. AMIA Annual Symposium Proceedings 2011, 2011: 723-31. PMID: 22195129, PMCID: PMC3243249.Peer-Reviewed Original ResearchMeSH KeywordsArtificial IntelligenceComputer SecurityElectronic Health RecordsHumansLogistic ModelsPrivacySensitivity and SpecificityConceptsSuspicious accessAccess recordsRule-based techniquesMachine learning methodsConstruction of classifiersAnomaly detectionInformative instancesLearning methodsSymbolic clusteringClassifier performanceSignature detectionIndependent test setInappropriate accessTest setEHRFiltering methodIntegrated filtering strategyFiltering strategyClassifierFilteringNegative rateFalse negative rateAccessDetectionClusteringUsing 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