Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry

Rios, R., Miller, R. J. H., Hu, L. H., Otaki, Y., Singh, A., Diniz, M., Sharir, T., Einstein, A. J., Fish, M. B., Ruddy, T. D., Kaufmann, P. A., Sinusas, A. J., Miller, E. J., Bateman, T. M., Dorbala, S., DiCarli, M., Van Kriekinge, S., Kavanagh, P., Parekh, T., … Slomka, P. (2021). Determining a minimum set of variables for machine learning cardiovascular event prediction: results from REFINE SPECT registry. Cardiovascular Research, 118(9), 2152–2164. https://doi.org/10.1093/cvr/cvab236
Authors:
Richard Rios
Robert J. H Miller
Lien Hsin Hu
Yuka Otaki
Ananya Singh
Marcio Diniz
Tali Sharir
Andrew J Einstein
Mathews B Fish
Terrence D Ruddy
Philipp A Kaufmann
Albert J Sinusas
Edward J Miller
Timothy M Bateman
Sharmila Dorbala
Marcelo Dicarli
Serge Van Kriekinge
Paul Kavanagh
Tejas Parekh
Joanna X Liang
Damini Dey
Daniel S Berman
Piotr Slomka
Affiliated Authors:
Andrew J Einstein
Author Keywords:
dimensionality reduction
machine learning
major adverse cardiovascular events
prognosis
spect myocardial perfusion imaging
Publication Type:
Article
Unique ID:
10.1093/cvr/cvab236
PMID:
Publication Date:
Data Source:
Scopus

Record Created: