2024
Develop 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
2017
A comparative study of different methods for automatic identification of clopidogrel-induced bleedings in electronic health records.
Lee H, Jiang M, Wu Y, Shaffer C, Cleator J, Friedman E, Lewis J, Roden D, Denny J, Xu H. A comparative study of different methods for automatic identification of clopidogrel-induced bleedings in electronic health records. AMIA Joint Summits On Translational Science Proceedings 2017, 2017: 185-192. PMID: 28815128, PMCID: PMC5543340.Peer-Reviewed Original ResearchElectronic health recordsAdverse drug reactionsMachine learning-based methodsLearning-based methodsHealth recordsRule-based methodReasonable recallAutomatic identificationValuable data sourceAutomatic methodTemporality informationCertain adverse drug reactionsData sourcesIdentification of patientsPharmacogenomic studiesManual chart reviewInformatics approachAdverse eventsChart reviewDrug reactionsHigh precisionFunction-based methodScoring methodDifferent typesBleeding