2014
“Big Data” and the Electronic Health Record
Ross M, Wei W, Ohno-Machado L. “Big Data” and the Electronic Health Record. Yearbook Of Medical Informatics 2014, 23: 97-104. PMID: 25123728, PMCID: PMC4287068, DOI: 10.15265/iy-2014-0003.Peer-Reviewed Reviews, Practice Guidelines, Standards, and Consensus StatementsConceptsBig dataEHR systemsElectronic health record systemsHealth record systemsData miningElectronic health recordsData applicationsActionable knowledgeMassive numberAdditional keywordsNew keywordsSecondary useInformatics professionalsHealth recordsRecord systemKeywordsLarge amountPrivacyNext stepMiningSecurityEHRSystemImplementationData
2013
Natural language processing: algorithms and tools to extract computable information from EHRs and from the biomedical literature
Ohno-Machado L, Nadkarni P, Johnson K. Natural language processing: algorithms and tools to extract computable information from EHRs and from the biomedical literature. Journal Of The American Medical Informatics Association 2013, 20: 805-805. PMID: 23935077, PMCID: PMC3756279, DOI: 10.1136/amiajnl-2013-002214.Commentaries, Editorials and Letters
2011
Anomaly 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 ResearchConceptsSuspicious accessAccess recordsRule-based techniquesMachine learning methodsConstruction of classifiersAnomaly detectionInformative instancesLearning methodsSymbolic clusteringClassifier performanceSignature detectionIndependent test setInappropriate accessTest setEHRFiltering methodIntegrated filtering strategyFiltering strategyClassifierFilteringNegative rateFalse negative rateAccessDetectionClustering