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of 165
pro vyhledávání: '"Roland, Albert"'
Autor:
Romero, Roland Albert A., Deypalan, Mariefel Nicole Y., Mehrotra, Suchit, Jungao, John Titus, Sheils, Natalie E., Manduchi, Elisabetta, Moore, Jason H.
We ascertain and compare the performances of AutoML tools on large, highly imbalanced healthcare datasets. We generated a large dataset using historical administrative claims including demographic information and flags for disease codes in four diffe
Externí odkaz:
http://arxiv.org/abs/2107.10495
Autor:
Roland Albert A. Romero, Mariefel Nicole Y. Deypalan, Suchit Mehrotra, John Titus Jungao, Natalie E. Sheils, Elisabetta Manduchi, Jason H. Moore
Publikováno v:
BioData Mining, Vol 15, Iss 1, Pp 1-13 (2022)
Abstract Objectives Ascertain and compare the performances of Automated Machine Learning (AutoML) tools on large, highly imbalanced healthcare datasets. Materials and Methods We generated a large dataset using historical de-identified administrative
Externí odkaz:
https://doaj.org/article/60b62052fe7d4169b8640d51fd19c755
Autor:
A. Romero, Roland Albert1 (AUTHOR), Y. Deypalan, Mariefel Nicole1 (AUTHOR), Mehrotra, Suchit1 (AUTHOR), Jungao, John Titus1 (AUTHOR), Sheils, Natalie E.1 (AUTHOR), Manduchi, Elisabetta2 (AUTHOR), Moore, Jason H.2 (AUTHOR) jason.moore@csmc.edu
Publikováno v:
BioData Mining. 7/26/2022, Vol. 15 Issue 1, p1-13. 13p.
Autor:
Joseph, Lee, Bashir, Mohammad, Xiang, Qun, Yerokun, Babatunde A., Matsouaka, Roland Albert, Vemulapalli, Sreekanth, Kapadia, Samir, Cigarroa, Joaquin E., Zahr, Firas
Publikováno v:
In JACC: Cardiovascular Interventions 9 April 2018 11(7):693-702
Autor:
Clarissa J. Diamantidis, David J. Cook, Stephan Dunning, Cyd Kristoff Redelosa, Martin Francis D. Bartolome, Roland Albert A. Romero, Joseph A. Vassalotti
Publikováno v:
Journal of General Internal Medicine. 37:4241-4247
Background Chronic kidney disease (CKD) is a common condition with adverse health outcomes addressable by early disease management. The impact of the COVID-19 pandemic on care utilization for the CKD population is unknown. Objective To examine pandem
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f2adb16d43f31bf52e208691a62604f4
https://doi.org/10.1007/s11606-022-07805-w
https://doi.org/10.1007/s11606-022-07805-w
Akademický článek
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Akademický článek
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Autor:
A. Romero, Roland Albert, Y. Deypalan, Mariefel Nicole, Mehrotra, Suchit, Jungao, John Titus, Sheils, Natalie E., Manduchi, Elisabetta, Moore, Jason H.
Additional file 1 ROC AUC performance of different AutoML models trained for various disease outcomes from non-stratified bootstrap samples. Median values are indicated by diamond markers and 95% CI limits are indicated by circles.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::386118a47a783b5e420bc4c48540e50c
Autor:
Roland, Albert E.
Thesis (Ph. D.)--University of Wisconsin--Madison, 1944.
Reprinted from the Proceedings of the Nova Scotian Institute of Science, vol. 21, pt. 3, 1944-1945. eContent provider-neutral record in process. Description based on print version record.
Reprinted from the Proceedings of the Nova Scotian Institute of Science, vol. 21, pt. 3, 1944-1945. eContent provider-neutral record in process. Description based on print version record.
Externí odkaz:
http://catalog.hathitrust.org/api/volumes/oclc/50027781.html
Autor:
Roland Albert A. Romero, Mariefel Nicole Y. Deypalan, Suchit Mehrotra, John Titus Jungao, Natalie E. Sheils, Elisabetta Manduchi, Jason H. Moore
We ascertain and compare the performances of AutoML tools on large, highly imbalanced healthcare datasets. We generated a large dataset using historical administrative claims including demographic information and flags for disease codes in four diffe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4916085ffe601fa6dc732d9d8ad13f30
http://arxiv.org/abs/2107.10495
http://arxiv.org/abs/2107.10495