R
Ralph B. D'Agostino
Researcher at Wake Forest University
Publications - 1336
Citations - 250792
Ralph B. D'Agostino is an academic researcher from Wake Forest University. The author has contributed to research in topics: Framingham Heart Study & Framingham Risk Score. The author has an hindex of 226, co-authored 1287 publications receiving 229636 citations. Previous affiliations of Ralph B. D'Agostino include VA Boston Healthcare System & University of Illinois at Urbana–Champaign.
Papers
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Journal ArticleDOI
Aging masks detection of radiation-induced brain injury
Lei Shi,John Olson,Ralph B. D'Agostino,Constance Linville,Michelle M. Nicolle,Michael E. Robbins,Kenneth T. Wheeler,Judy K. Brunso-Bechtold +7 more
TL;DR: Age-dependent changes in these parameters appear to mask their detection in old rats, a phenomenon also likely to occur in elderly fWBI patients >70 years of age.
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Non-invasive Longitudinal Tracking of Human Amniotic Fluid Stem Cells in the Mouse Heart
Dawn M. Delo,John Olson,Pedro M. Baptista,Ralph B. D'Agostino,Anthony Atala,Jian Ming Zhu,Shay Soker +6 more
TL;DR: The results indicate that high resolution MRI can be used successfully for noninvasive longitudinal tracking of hAFS cells injected in the mouse heart and might serve to monitor cell survival, proliferation and integration into myocardial tissue.
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A Proposal for Integrated Efficacy-to-Effectiveness (E2E) Clinical Trials
Harry P. Selker,Kenneth A. Oye,Hans-Georg Eichler,Norman Stockbridge,Cyrus R. Mehta,Kenneth I. Kaitin,Newell E. McElwee,Peter K. Honig,John K. Erban,Ralph B. D'Agostino +9 more
TL;DR: In this paper, the authors propose an "efficacy-to-effectiveness" (E2E) clinical trial design, in which an effectiveness trial would commence seamlessly upon completion of the efficacy trial.
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Equivalence of improvement in area under ROC curve and linear discriminant analysis coefficient under assumption of normality
TL;DR: This paper demonstrates that under the assumption of multivariate normality and employing linear discriminant analysis (LDA) to construct the risk prediction tool, statistical significance of the new predictor(s) is equivalent to the statistical importance of the increase in AUC.
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Evaluating classification accuracy for modern learning approaches.
TL;DR: A tutorial for evaluating classification accuracy for various state‐of‐the‐art learning approaches, including familiar shallow and deep learning methods, and offers problem‐based R code to illustrate how to perform these statistical computations step by step.