Efficacy of Dynamics-Based Features for Machine Learning Classification of Renal Hemodynamics-Reference-Cited by-同舟云学术

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Efficacy of Dynamics-Based Features for Machine Learning Classification of Renal Hemodynamics

Published:2023-09-04 Issue: Volume: Page:
ISSN:
Container-title:2023 31st European Signal Processing Conference (EUSIPCO)
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Author:

Chopde Purva R.¹,Álvarez-Cedrón Rocío²,Alphonse Sebastian¹,Polichnowski Aaron J.³,Griffin Karen A.⁴,Williamson Geoffrey A.¹

Affiliation:

1. Illinois Institute of Technology,Dept. of Elec. and Comp. Engr.,Chicago,IL,U.S.A.

2. Illinois Institute of Technology,Chicago,IL,U.S.A.

3. East Tennessee State University,Dept. of Biomedical Sciences,Johnson City,TN,U.S.A.

4. Loyola Univ. Med. Ctr. and Hines VA Hosp.,Department of Medicine,Maywood,IL,U.S.A.

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10289698/10289713/10289999.pdf?arnumber=10289999

Reference32 articles.

1. Effects of calcium channel blockers on “dynamic” and “steady-state step” renal autoregulation

2. Assessment of renal autoregulation

3. Reduced Complexity Volterra Models for Nonlinear System Identification

4. Large BP-dependent and -independent differences in susceptibility to nephropathy after nitric oxide inhibition in Sprague-Dawley rats from two major suppliers

5. Machine Learning for Renal Pathologies: An Updated Survey

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