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Matematicheskaya Biologiya i Bioinformatika, 2020, Volume 15, Issue 1, Pages 40–56
DOI: https://doi.org/10.17537/2020.15.40
(Mi mbb421)
 

This article is cited in 2 scientific papers (total in 2 papers)

Information and Computer Technologies in Biology and Medicine

Artificial neural networks in cardiology: analysis of numerical and text data

P. S. Onishchenkoab, K. Yu. Klyshnikovb, E. A. Ovcharenkob

a Institute of Computational Technologies, Siberian Branch of the Russian Academy of Sciences, Novosibirsk, Russia
b Kuzbass Cardiology Center, Kemerovo, Russia
Full-text PDF (736 kB) Citations (2)
References:
Abstract: This review discusses works on the use of artificial neural networks for processing numerical and textual data. Application of a number of widely used approaches is considered, such as decision support systems; prediction systems, providing forecasts of outcomes of various methods of treatment of cardiovascular diseases, and risk assessment systems. The possibility of using artificial neural networks as an alternative approach to standard methods for processing patient clinical data has been shown. The use of neural network technologies in the creation of automated assistants to the attending physician will make it possible to provide medical services better and more efficiently.
Key words: cardiology, cardiovascular system, cardiovascular diseases, artificial neural networks.
Funding agency Grant number
Ministry of Education and Science of the Russian Federation 0546-2015-0011
Received 18.07.2019, 15.01.2020, Published 18.02.2020
Document Type: Article
Language: Russian
Citation: P. S. Onishchenko, K. Yu. Klyshnikov, E. A. Ovcharenko, “Artificial neural networks in cardiology: analysis of numerical and text data”, Mat. Biolog. Bioinform., 15:1 (2020), 40–56
Citation in format AMSBIB
\Bibitem{OniKlyOvc20}
\by P.~S.~Onishchenko, K.~Yu.~Klyshnikov, E.~A.~Ovcharenko
\paper Artificial neural networks in cardiology: analysis of numerical and text data
\jour Mat. Biolog. Bioinform.
\yr 2020
\vol 15
\issue 1
\pages 40--56
\mathnet{http://mi.mathnet.ru/mbb421}
\crossref{https://doi.org/10.17537/2020.15.40}
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  • This publication is cited in the following 2 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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