2021
Comprehensive Characterization of COVID-19 Patients with Repeatedly Positive SARS-CoV-2 Tests Using a Large U.S. Electronic Health Record Database
Dong X, Zhou Y, Shu X, Bernstam E, Stern R, Aronoff D, Xu H, Lipworth L. Comprehensive Characterization of COVID-19 Patients with Repeatedly Positive SARS-CoV-2 Tests Using a Large U.S. Electronic Health Record Database. Microbiology Spectrum 2021, 9: 10.1128/spectrum.00327-21. PMID: 34406805, PMCID: PMC8552669, DOI: 10.1128/spectrum.00327-21.Peer-Reviewed Original ResearchConceptsPositive SARS-CoV-2 testSARS-CoV-2 testSecond positive testElectronic health record databaseCases of reinfectionHealth record databasePositive testPositive SARS-CoV-2 PCR test resultsSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testingSARS-CoV-2 PCR test resultsRecord databaseSevere acute respiratory syndrome coronavirus 2Intensive care unit admissionAcute respiratory syndrome coronavirus 2SARS-CoV-2 infectionRespiratory syndrome coronavirus 2Long-term health consequencesLarge electronic health record databasePotential long-term health consequencesCare unit admissionOverweight/obeseChronic medical conditionsPositive molecular testCOVID-19 patientsSyndrome coronavirus 2Leveraging a health information exchange for analyses of COVID-19 outcomes including an example application using smoking history and mortality
Tortolero G, Brown M, Sharma S, de Oliveira Otto M, Yamal J, Aguilar D, Gunther M, Mofleh D, Harris R, John J, de Vries P, Ramphul R, Serbo D, Kiger J, Banerjee D, Bonvino N, Merchant A, Clifford W, Mikhail J, Xu H, Murphy R, Wei Q, Vahidy F, Morrison A, Boerwinkle E. Leveraging a health information exchange for analyses of COVID-19 outcomes including an example application using smoking history and mortality. PLOS ONE 2021, 16: e0247235. PMID: 34081724, PMCID: PMC8174716, DOI: 10.1371/journal.pone.0247235.Peer-Reviewed Original ResearchConceptsBody mass indexCOVID-19 patientsRisk factorsTobacco useCOVID-19 fatalitiesHealth information exchangeRace/ethnicityCOVID-19Laboratory risk factorsNumber of comorbiditiesCOVID-19 cohortMultivariable logistic regressionImportant risk factorPotential risk factorsCOVID-19 outcomesFormer tobacco usersTobacco use historyLarge health information exchangeMass indexElectronic health record systemsUnfavorable outcomeClinical dataTobacco usersOutcome analysisElectronic health information
2013
ICD-9 tobacco use codes are effective identifiers of smoking status
Wiley L, Shah A, Xu H, Bush W. ICD-9 tobacco use codes are effective identifiers of smoking status. Journal Of The American Medical Informatics Association 2013, 20: 652-658. PMID: 23396545, PMCID: PMC3721171, DOI: 10.1136/amiajnl-2012-001557.Peer-Reviewed Original ResearchConceptsSmoking statusClinic populationGeneral clinic populationClinical populationsSmoking cessation attemptCancer-free controlsElectronic health recordsFull-text recordsCancer casesCessation attemptsTobacco useInternational ClassificationSmokersHealth recordsGenetic association studiesGenetic association analysisStatusPopulationLittle evidenceBiorepositoryInitial success
2012
A study of transportability of an existing smoking status detection module across institutions.
Liu M, Shah A, Jiang M, Peterson N, Dai Q, Aldrich M, Chen Q, Bowton E, Liu H, Denny J, Xu H. A study of transportability of an existing smoking status detection module across institutions. AMIA Annual Symposium Proceedings 2012, 2012: 577-86. PMID: 23304330, PMCID: PMC3540509.Peer-Reviewed Original ResearchMeSH KeywordsElectronic Health RecordsHumansMedical Record LinkageNatural Language ProcessingSmokingConceptsDetection moduleNatural language processing systemsKnowledge Extraction SystemEMR dataRule-based classifierClinical Text AnalysisHighest F-measureLanguage processing systemElectronic medical recordsF-measureLevels of classificationProcessing systemSpecific tasksText analysisClassifierDesirable performanceModuleModest effortExtraction systemCTAKESSmoking moduleMachineSystemTaskClassification