Chenxi Huang
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Cardiovascular Medicine
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Associate Research Scientist
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Chenxi Huang finished her PhD program in Biomedical Engineering at Yale University in 2015. Her PhD thesis focuses on dealing with outliers in cryo-EM reconstruction of large molecules. Her research interests are fundamental issues of and innovative mathematical and computational approaches to biomedical data analysis, identification and integration of critical information in and across various imaging modalities, and sparse representations in detection and estimation for massive high-dimensional and noisy data. Prior to her PhD, she received her bachelor degree in Information Engineering from Shanghai Jiaotong University and Master of Science in Electrical Engineering from Yale University.
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Cardiovascular Medicine
Associate Research ScientistPrimary
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Yale Co-Authors
Frequent collaborators of Chenxi Huang's published research.
Publications Timeline
A big-picture view of Chenxi Huang's research output by year.
Harlan Krumholz, MD, SM
Yuan Lu, ScD
Karthik Murugiah, MBBS, FACC, FSCAI
Mitsuaki Sawano, MD, PhD
Rohan Khera, MD, MS
Akiko Iwasaki, PhD
13Publications
29Citations
Publications
2024
Long COVID Characteristics and Experience: A Descriptive Study from the Yale LISTEN Research Cohort
Sawano M, Wu Y, Shah R, Zhou T, Arun A, Khosla P, Kaleem S, Vashist A, Bhattacharjee B, Ding Q, Lu Y, Caraballo C, Warner F, Huang C, Herrin J, Putrino D, Michelsen T, Fisher L, Adinig C, Iwasaki A, Krumholz H. Long COVID Characteristics and Experience: A Descriptive Study from the Yale LISTEN Research Cohort. The American Journal Of Medicine 2024 PMID: 38663793, DOI: 10.1016/j.amjmed.2024.04.015.Peer-Reviewed Original ResearchAltmetricConceptsExperiences of peopleHealth statusLong COVIDLower health statusNew-onset conditionsCommunity support servicesNon-Hispanic whitesArray of healthQuality of lifeVisual analogue scaleMental healthPsychological distressPsychological statusDescriptive studyHealthcare systemMedian scoreSupport servicesResearch cohortSocial isolationDemographic characteristicsAnalogue scaleImpact of long COVIDHealthFinancial stressParticipantsHeterogeneity in the Prognosis of Acute Kidney Injury Following Percutaneous Coronary Intervention
Hu J, Murugiah K, Xin X, Sawano M, Lu Y, Wilson F, Masoudi F, Messenger J, Krumholz H, Huang C. Heterogeneity in the Prognosis of Acute Kidney Injury Following Percutaneous Coronary Intervention. Journal Of The American Heart Association 2024, 13: e033649. PMID: 38390832, PMCID: PMC10944032, DOI: 10.1161/jaha.123.033649.Peer-Reviewed Original ResearchAltmetric
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 ResearchCitationsAltmetricMeSH Keywords and ConceptsNonexercise 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 ResearchAltmetricQuantifying 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 ResearchCitationsAltmetricMeSH Keywords and ConceptsConceptsRetrospective 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
2021
A Simple Recovery Framework for Signals with Time-Varying Sparse Support
Durgin N, Grotheer R, Huang C, Li S, Ma A, Needell D, Qin J. A Simple Recovery Framework for Signals with Time-Varying Sparse Support. Association For Women In Mathematics Series 2021, 26: 211-230. DOI: 10.1007/978-3-030-79891-8_9.Peer-Reviewed Original ResearchStochastic greedy algorithms for multiple measurement vectors
Qin J, Li S, Needell D, Ma A, Grotheer R, Huang C, Durgin N. Stochastic greedy algorithms for multiple measurement vectors. Inverse Problems And Imaging 2021, 15: 79-107. DOI: 10.3934/ipi.2020066.Peer-Reviewed Original ResearchCitations
2019
Jointly Sparse Signal Recovery with Prior Info
Durgin N, Grotheer R, Huang C, Li S, Ma A, Needell D, Qin J. Jointly Sparse Signal Recovery with Prior Info. 2019, 00: 645-649. DOI: 10.1109/ieeeconf44664.2019.9048818.Peer-Reviewed Original ResearchCitationsFast 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 ResearchCitationsMeSH Keywords and ConceptsConceptsDiffuse 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 algorithmSparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors
Durgin N, Grotheer R, Huang C, Li S, Ma A, Needell D, Qin J. Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors. Association For Women In Mathematics Series 2019, 17: 1-14. DOI: 10.1007/978-3-030-11566-1_1.Peer-Reviewed Original ResearchCitations