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Machine Learning Models Enhance Prediction of Arrhythmogenic Right Ventricular Cardiomyopathy.

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第一作者： Kwaku K,Quansah

第一单位： Division of Cardiology, Johns Hopkins School of Medicine Baltimore, MD 21202.;Department of Biomedical Engineering, Johns Hopkins University Baltimore, MD 21202.;Cellular and Molecular Medicine Program, Johns Hopkins University Baltimore, MD 21202.;Department of Cell Biology, Johns Hopkins University Baltimore, MD 21202.;Institute for Cell Engineering, Johns Hopkins University Baltimore, MD 21202.;Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, MD 21202.

作者： Kwaku K,Quansah ^[1] ; Sean A,Murphy ^[1] ; Esther,Kwon ^[1] ; Emma,Anderson ^[1] ; Crystal,Tichnell ^[2] ; Brittney,Murray ^[2] ; Sean,Gaines ^[2] ; Alessio,Gasperetti ^[2] ; Cynthia A,James ^[2] ; Hugh,Calkins ^[2] ; Richard T,Carrick ^[2] ; Chulan,Kwon ^[1]

作者单位： Division of Cardiology, Johns Hopkins School of Medicine Baltimore, MD 21202.;Department of Biomedical Engineering, Johns Hopkins University Baltimore, MD 21202.;Cellular and Molecular Medicine Program, Johns Hopkins University Baltimore, MD 21202.;Department of Cell Biology, Johns Hopkins University Baltimore, MD 21202.;Institute for Cell Engineering, Johns Hopkins University Baltimore, MD 21202.;Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, MD 21202. ^[1] Division of Cardiology, Johns Hopkins School of Medicine Baltimore, MD 21202.;Heart and Vascular Institute, Johns Hopkins Hospital, Baltimore, MD 21202. ^[2]

关键词 Arrhythmogenic Right Ventricular Cardiomyopathy Gradient Boosted Trees Machine Learning clinical decision-making diagnostic support matrix clinical data predictive modeling

DOI 10.1101/2025.06.16.25329706

PMID 40585140

发布时间 2025-10-16

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