2024
Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks
Lu Y, Keeley E, Barrette E, Cooper-DeHoff R, Dhruva S, Gaffney J, Gamble G, Handke B, Huang C, Krumholz H, McDonough C, Schulz W, Shaw K, Smith M, Woodard J, Young P, Ervin K, Ross J. Use of electronic health records to characterize patients with uncontrolled hypertension in two large health system networks. BMC Cardiovascular Disorders 2024, 24: 497. PMID: 39289597, PMCID: PMC11409735, DOI: 10.1186/s12872-024-04161-x.Peer-Reviewed Original ResearchConceptsElectronic health recordsHealth recordsHealth systemUncontrolled hypertensionUse of electronic health recordsHypertension managementElectronic health record systemsOneFlorida Clinical Research ConsortiumElectronic health record dataYale New Haven Health SystemBP measurementsICD-10-CM codesHealth system networkPublic health priorityICD-10-CMIncidence rate of deathElevated BP measurementsElevated blood pressure measurementsHealthcare visitsAmbulatory careHealth priorityRetrospective cohort studyEHR dataOneFloridaBlood pressure measurements
2023
Effect of the New Glomerular Filtration Rate Estimation Equation on Risk Predicting Models for Acute Kidney Injury After Percutaneous Coronary Intervention
Huang C, Murugiah K, Li X, Masoudi F, Messenger J, Williams K, Mortazavi B, Krumholz H. Effect of the New Glomerular Filtration Rate Estimation Equation on Risk Predicting Models for Acute Kidney Injury After Percutaneous Coronary Intervention. Circulation Cardiovascular Interventions 2023, 16: e012831. PMID: 37009734, PMCID: PMC10622038, DOI: 10.1161/circinterventions.122.012831.Peer-Reviewed Original ResearchNonexercise machine learning models for maximal oxygen uptake prediction in national population surveys.
Liu Y, Herrin J, Huang C, Khera R, Dhingra L, Dong W, Mortazavi B, Krumholz H, Lu Y. Nonexercise machine learning models for maximal oxygen uptake prediction in national population surveys. Journal Of The American Medical Informatics Association 2023, 30: 943-952. PMID: 36905605, PMCID: PMC10114129, DOI: 10.1093/jamia/ocad035.Peer-Reviewed Original ResearchQuantifying Blood Pressure Visit-to-Visit Variability in the Real-World Setting: A Retrospective Cohort Study
Lu Y, Linderman G, Mahajan S, Liu Y, Huang C, Khera R, Mortazavi B, Spatz E, Krumholz H. Quantifying Blood Pressure Visit-to-Visit Variability in the Real-World Setting: A Retrospective Cohort Study. Circulation Cardiovascular Quality And Outcomes 2023, 16: e009258. PMID: 36883456, DOI: 10.1161/circoutcomes.122.009258.Peer-Reviewed Original ResearchConceptsRetrospective cohort studyBlood pressure valuesPatient characteristicsReal-world settingCohort studyPatient subgroupsYale New Haven Health SystemMean body mass indexSystolic blood pressure valuesBlood pressure visitHistory of hypertensionCoronary artery diseaseManagement of patientsMultivariable linear regression modelsBlood pressure readingsBody mass indexPatient-level measuresBlood pressure variationAbsolute standardized differencesNon-Hispanic whitesAntihypertensive medicationsReal-world practiceVisit variabilityArtery diseaseRegression models
2019
Fast Hyperspectral Diffuse Optical Imaging Method with Joint Sparsity
Durgin N, Grotheer R, Huang C, Li S, Ma A, Needell D, Qin J. Fast Hyperspectral Diffuse Optical Imaging Method with Joint Sparsity. Annual International Conference Of The IEEE Engineering In Medicine And Biology Society (EMBC) 2019, 00: 4758-4761. PMID: 31946925, DOI: 10.1109/embc.2019.8857069.Peer-Reviewed Original ResearchConceptsDiffuse optical tomographyDOT inverse problemHigh reconstruction accuracyJoint sparsityMultiple measurement vector (MMV) problemOptical imaging methodsNumerical resultsOptical tomographyGradient descent methodReconstruction accuracyAbsorption coefficientDOT dataNumber of wavelengthsDOT imagesImportant functional imaging modalityInverse problemImaging methodDescent methodFunctional imaging modalitiesStochastic greedy algorithm