Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Rebabonye Pharithi"'
Autor:
Mohammed Ali, Haaris A. Shiwani, Mohammed Y. Elfaki, Moaz Hamid, Rebabonye Pharithi, Rene Kamgang, Christian BinounA Egom, Jean Louis Essame Oyono, Emmanuel Eroume-A Egom
Publikováno v:
The Egyptian Heart Journal, Vol 74, Iss 1, Pp 1-9 (2022)
Abstract Myocarditis has been discovered to be a significant complication of coronavirus disease 2019 (COVID-19), a condition caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus. COVID-19 myocarditis seems to have distinc
Externí odkaz:
https://doaj.org/article/a062131c24174552b737d8b0b6a567fd
Autor:
Dafni K. Plati, Evanthia E. Tripoliti, Aris Bechlioulis, Aidonis Rammos, Iliada Dimou, Lampros Lakkas, Chris Watson, Ken McDonald, Mark Ledwidge, Rebabonye Pharithi, Joe Gallagher, Lampros K. Michalis, Yorgos Goletsis, Katerina K. Naka, Dimitrios I. Fotiadis
Publikováno v:
Diagnostics, Vol 11, Iss 10, p 1863 (2021)
The aim of this study was to address chronic heart failure (HF) diagnosis with the application of machine learning (ML) approaches. In the present study, we simulated the procedure that is followed in clinical practice, as the models we built are bas
Externí odkaz:
https://doaj.org/article/24103b5bb3184ec78d580258f260cefb
Autor:
Theofilos G, Papadopoulos, Daphni, Plati, Evanthia E, Tripoliti, Yorgos, Goletsis, Katerina K, Naka, Aidonis, Rammos, Aris, Bechlioulis, Chris, Watson, Kenneth, McDonald, Mark, Ledwidge, Rebabonye, Pharithi, Joseph, Gallagher, Dimitrios I, Fotiadis
Publikováno v:
2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).
The aim of the study is to address the heart failure (HF) diagnosis with the application of deep learning approaches. Seven deep learning architectures are implemented, where stacked Restricted Boltzman Machines (RBMs) and stacked Autoencoders (AEs)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::18c7b1e73a2c486c522b6d22d782310a
https://doi.org/10.1109/embc46164.2021.9630409
https://doi.org/10.1109/embc46164.2021.9630409
Autor:
Mark Ledwidge, Yorgos Goletsis, Evanthia E. Tripoliti, Dimitrios I. Fotiadis, Ken Mcdonald, Joseph P. Gallagher, Katerina K. Naka, Chris Watson, Iliada Dimou, Aidonis Rammos, Lampros Lakkas, Aris Bechlioulis, Rebabonye Pharithi, Dafni K. Plati, Lampros K. Michalis
Publikováno v:
Diagnostics
Volume 11
Issue 10
Diagnostics, Vol 11, Iss 1863, p 1863 (2021)
Plati, D K, Tripoliti, E E, Bechlioulis, A, Rammos, A, Dimou, I, Lakkas, L, Watson, C, McDonald, K, Ledwidge, M, Pharithi, R, Gallagher, J, Michalis, L K, Goletsis, Y, Naka, K K & Fotiadis, D I 2021, ' A Machine Learning Approach for Chronic Heart Failure Diagnosis ', Diagnostics, vol. 11, no. 10, 1863 . https://doi.org/10.3390/diagnostics11101863
Volume 11
Issue 10
Diagnostics, Vol 11, Iss 1863, p 1863 (2021)
Plati, D K, Tripoliti, E E, Bechlioulis, A, Rammos, A, Dimou, I, Lakkas, L, Watson, C, McDonald, K, Ledwidge, M, Pharithi, R, Gallagher, J, Michalis, L K, Goletsis, Y, Naka, K K & Fotiadis, D I 2021, ' A Machine Learning Approach for Chronic Heart Failure Diagnosis ', Diagnostics, vol. 11, no. 10, 1863 . https://doi.org/10.3390/diagnostics11101863
The aim of this study was to address chronic heart failure (HF) diagnosis with the application of machine learning (ML) approaches. In the present study, we simulated the procedure that is followed in clinical practice, as the models we built are bas
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5b1d7ea70087bd1f63c37b34b0b2e108
http://europepmc.org/articles/PMC8534549
http://europepmc.org/articles/PMC8534549