2025
Human Immunodeficiency Virus Infection and Long COVID: A RECOVER Program, Electronic Health Record–Based Cohort Study
Hawkins K, Dandachi D, Verzani Z, Brannock M, Lewis C, Abedian S, Jaferian S, Wuller S, Truong J, Witvliet M, Dymond G, Mehta H, Patel P, Hill E, Weiner M, Carton T, Kaushal R, Feuerriegel E, Tran H, Marks K, Oliveira C, Gardner E, Ofotokun I, Gulick R, Erlandson K. Human Immunodeficiency Virus Infection and Long COVID: A RECOVER Program, Electronic Health Record–Based Cohort Study. Clinical Infectious Diseases 2025, ciaf242. PMID: 40354184, DOI: 10.1093/cid/ciaf242.Peer-Reviewed Original ResearchDefinition of Long COVIDICD-10Baseline chronic conditionsLong COVIDElectronic health record databaseIncreased riskHealth record databaseICD-10 codesICD-10 definitionRisk of long COVIDClinical Research NetworkChronic conditionsAcute SARS-CoV-2 infectionHIV statusAnalytic sampleOdds ratioCohort definitionsCohort CollaborationComputable phenotypePhenotype definitionRecord databaseSARS-CoV-2 infectionLogistic regressionResearch NetworkMulticenter studyAssessment of health conditions from patient electronic health record portals vs self-reported questionnaires: an analysis of the INSPIRE study
Khera R, Sawano M, Warner F, Coppi A, Pedroso A, Spatz E, Yu H, Gottlieb M, Saydah S, Stephens K, Rising K, Elmore J, Hill M, Idris A, Montoy J, O’Laughlin K, Weinstein R, Venkatesh A, Weinstein R, Gottlieb M, Santangelo M, Koo K, Derden A, Gottlieb M, Gatling K, Ahmed Z, Gomez C, Guzman D, Hassaballa M, Jerger R, Kaadan A, Venkatesh A, Spatz E, Kinsman J, Malicki C, Lin Z, Li S, Yu H, Mannan I, Yang Z, Liu M, Venkatesh A, Spatz E, Ulrich A, Kinsman J, Malicki C, Dorney J, Pierce S, Puente X, Salah W, Nichol G, Stephens K, Anderson J, Schiffgens M, Morse D, Adams K, Stober T, Maat Z, O’Laughlin K, Gentile N, Geyer R, Willis M, Zhang Z, Chang G, Lyon V, Klabbers R, Ruiz L, Malone K, Park J, Rising K, Kean E, Chang A, Renzi N, Watts P, Kelly M, Schaeffer K, Grau D, Cheng D, Shutty C, Charlton A, Shughart L, Shughart H, Amadio G, Miao J, Hannikainen P, Elmore J, Wisk L, L’Hommedieu M, Chandler C, Eguchi M, Roldan K, Moreno R, Rodriguez R, Wang R, Montoy J, Kemball R, Chan V, Chavez C, Wong A, Arreguin M, Hill M, Site R, Kane A, Nikonowicz P, Sapp S, Idris A, McDonald S, Gallegos D, Martin K, Saydah S, Plumb I, Hall A, Briggs-Hagen M. Assessment of health conditions from patient electronic health record portals vs self-reported questionnaires: an analysis of the INSPIRE study. Journal Of The American Medical Informatics Association 2025, 32: 784-794. PMID: 40036551, PMCID: PMC12012333, DOI: 10.1093/jamia/ocaf027.Peer-Reviewed Original ResearchElectronic health recordsSelf-report questionnairesSelf-ReportHealth conditionsElectronic health record portalsElectronic health record platformsEHR elementsSelf-reported health conditionsElectronic health record dataSelf-reported conditionsAssessment of health conditionEvaluation of health conditionsPrevalence of conditionsPatient portalsTraditional self-reportPrevalence of comorbiditiesHealth recordsEHR dataEHR phenotypesDiagnosis codesHospitalization riskComputable phenotypeNationwide studyCohen's kappaPatient characteristicsLong COVID Incidence Proportion in Adults and Children Between 2020 and 2024: An Electronic Health Record-Based Study From the RECOVER Initiative
Mandel H, Yoo Y, Allen A, Abedian S, Verzani Z, Karlson E, Kleinman L, Mudumbi P, Oliveira C, Muszynski J, Gross R, Carton T, Kim C, Taylor E, Park H, Divers J, Kelly J, Arnold J, Geary C, Zang C, Tantisira K, Rhee K, Koropsak M, Mohandas S, Vasey A, Mosa A, Haendel M, Chute C, Murphy S, O'Brien L, Szmuszkovicz J, Guthe N, Santana J, De A, Bogie A, Halabi K, Mohanraj L, Kinser P, Packard S, Tuttle K, Hirabayashi K, Kaushal R, Pfaff E, Weiner M, Thorpe L, Moffitt R. Long COVID Incidence Proportion in Adults and Children Between 2020 and 2024: An Electronic Health Record-Based Study From the RECOVER Initiative. Clinical Infectious Diseases 2025, 80: 1247-1261. PMID: 39907495, PMCID: PMC12272849, DOI: 10.1093/cid/ciaf046.Peer-Reviewed Original ResearchElectronic health recordsIncidence proportionIncidence estimatesComputable phenotypeEHR-based studiesLong COVIDPublic health priorityPost-acute sequelae of SARS-CoV-2 infectionHealth recordsSequelae of SARS-CoV-2 infectionHealth priorityProportion of individualsRetrospective cohort studyHarmonized definitionExcess incidenceCohort studyImprove preventionSARS-CoV-2 infectionResearch NetworkRisk factorsAdultsCOVID-19Control groupAcute SARS-CoV-2 infectionPediatric population
2024
Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data
Lu Y, Tong J, Chubak J, Lumley T, Hubbard R, Xu H, Chen Y. Leveraging error-prone algorithm-derived phenotypes: Enhancing association studies for risk factors in EHR data. Journal Of Biomedical Informatics 2024, 157: 104690. PMID: 39004110, DOI: 10.1016/j.jbi.2024.104690.Peer-Reviewed Original ResearchElectronic health recordsElectronic health record dataKaiser Permanente WashingtonEHR-derived phenotypesAssociation studiesHealth recordsColon cancer recurrencePhenotyping errorsComputable phenotypeRisk factorsCancer recurrenceMultiple phenotypesReduce biasImprove estimation accuracySimulation studyBias reductionKaiserReduction of biasBiasEstimation accuracyAssociationStudyOutcomesRiskEstimation efficiencyDevelop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data
He X, Wei R, Huang Y, Chen Z, Lyu T, Bost S, Tong J, Li L, Zhou Y, Li Z, Guo J, Tang H, Wang F, DeKosky S, Xu H, Chen Y, Zhang R, Xu J, Guo Y, Wu Y, Bian J. Develop and validate a computable phenotype for the identification of Alzheimer's disease patients using electronic health record data. Alzheimer's & Dementia Diagnosis Assessment & Disease Monitoring 2024, 16: e12613. PMID: 38966622, PMCID: PMC11220631, DOI: 10.1002/dad2.12613.Peer-Reviewed Original ResearchElectronic health record dataElectronic health recordsComputable phenotypeHealth record dataManual chart reviewHealth recordsAlzheimer's diseaseDiagnosis codesRecord dataChart reviewUTHealthAlzheimer's disease patientsUniversity of MinnesotaAD diagnosisAD identificationDisease patientsPatientsAlzheimerAD patientsDemographicsDiagnosisDiseaseCodeDataUniversity
2021
Feasibility of capturing real-world data from health information technology systems at multiple centers to assess cardiac ablation device outcomes: A fit-for-purpose informatics analysis report
Jiang G, Dhruva SS, Chen J, Schulz WL, Doshi AA, Noseworthy PA, Zhang S, Yu Y, Young H, Brandt E, Ervin KR, Shah ND, Ross JS, Coplan P, Drozda JP. Feasibility of capturing real-world data from health information technology systems at multiple centers to assess cardiac ablation device outcomes: A fit-for-purpose informatics analysis report. Journal Of The American Medical Informatics Association 2021, 28: 2241-2250. PMID: 34313748, PMCID: PMC8449615, DOI: 10.1093/jamia/ocab117.Peer-Reviewed Original ResearchConceptsReal-world dataHealth information technology systemsInformation technology systemsUnique device identifiersMaturity modelNatural language processing toolsTechnology systemsUnstructured data elementsNatural language processingCommon data modelData quality frameworkLanguage processing toolsComputable phenotypeInformatics approachElectronic health recordsClinical data systemsData modelLanguage processingDevice identifiersStandardized codesData elementsProcessing toolsInformatics technologiesData captureHealth records
2020
Progress Report on EMBED: A Pragmatic Trial of User-Centered Clinical Decision Support to Implement EMergency Department-Initiated BuprenorphinE for Opioid Use Disorder †
Melnick ER, Nath B, Ahmed OM, Brandt C, Chartash D, Dziura JD, Hess EP, Holland WC, Hoppe JA, Jeffery MM, Katsovich L, Li F, Lu CC, Maciejewski K, Maleska M, Mao JA, Martel S, Michael S, Paek H, Patel MD, Platts-Mills TF, Rajeevan H, Ray JM, Skains RM, Soares WE, Deutsch A, Solad Y, D’Onofrio G. Progress Report on EMBED: A Pragmatic Trial of User-Centered Clinical Decision Support to Implement EMergency Department-Initiated BuprenorphinE for Opioid Use Disorder †. Journal Of Psychiatry And Brain Science 2020, 2: e200003. PMID: 32309637, PMCID: PMC7164817, DOI: 10.20900/jpbs.20200003.Peer-Reviewed Original ResearchBuprenorphine/naloxoneOpioid use disorderClinical decision supportPragmatic trialElectronic health recordsUse disordersEmergency department-initiated buprenorphineMulti-centre pragmatic trialRoutine emergency careHealthcare systemRates of EDNaloxone prescribingPilot testingSingle EDEmergency departmentPhysicians' perceptionsEmergency careMortality rateEarly identificationComputable phenotypeUnique physiciansInformed consentCare paradigmHealth recordsIntervention effectiveness
2019
Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record–Based Computable Phenotype Derivation and Validation Study
Chartash D, Paek H, Dziura JD, Ross BK, Nogee DP, Boccio E, Hines C, Schott AM, Jeffery MM, Patel MD, Platts-Mills TF, Ahmed O, Brandt C, Couturier K, Melnick E. Identifying Opioid Use Disorder in the Emergency Department: Multi-System Electronic Health Record–Based Computable Phenotype Derivation and Validation Study. JMIR Medical Informatics 2019, 7: e15794. PMID: 31674913, PMCID: PMC6913746, DOI: 10.2196/15794.Peer-Reviewed Original ResearchOpioid use disorderNegative predictive valuePositive predictive valueEmergency department patientsEmergency departmentUse disordersHealth care systemPredictive valueComputable phenotypeExternal validation phasesDepartment patientsCare systemPhysician chart reviewLarge health care systemExternal validation cohortEmergency medicine physiciansHigh predictive valueElectronic health recordsChart reviewChief complaintValidation cohortPragmatic trialClinical dataBilling codesMedicine physicians
2018
Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering
Tang M, Gao C, Goutman S, Kalinin A, Mukherjee B, Guan Y, Dinov I. Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering. Neuroinformatics 2018, 17: 407-421. PMID: 30460455, PMCID: PMC6527505, DOI: 10.1007/s12021-018-9406-9.Peer-Reviewed Original ResearchConceptsAmyotrophic Lateral Sclerosis Functional Rating ScaleClusters of participantsModel-basedAmyotrophic lateral sclerosisRating ScaleComputable phenotypeFunctional Rating ScaleSets of featuresUnsupervised clusteringUnsupervised machine learning methodsClinical decision supportMachine learning methods
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