Zobrazeno 1 - 10
of 21
pro vyhledávání: '"Nataliia Melnykova"'
Autor:
Yaroslav Tolstyak, Rostyslav Zhuk, Igor Yakovlev, Nataliya Shakhovska, Michal Gregus ml, Valentyna Chopyak, Nataliia Melnykova
Publikováno v:
Applied Sciences, Vol 11, Iss 21, p 10380 (2021)
Machine learning is used to develop predictive models to diagnose different diseases, particularly kidney transplant survival prediction. The paper used the collected dataset of patients’ individual parameters to predict the critical risk factors a
Externí odkaz:
https://doaj.org/article/a8b1de2a13dc480cba6a74e66d0074a0
Autor:
Nataliia Melnykova, Nataliya Shakhovska, Volodymyr Melnykov, Kateryna Melnykova, Khrystyna Lishchuk-Yakymovych
Publikováno v:
Big Data and Cognitive Computing, Vol 5, Iss 3, p 37 (2021)
The paper describes the medical data personalization problem by determining the individual characteristics needed to predict the number of days a patient spends in a hospital. The mathematical problem of patient information analysis is formalized, wh
Externí odkaz:
https://doaj.org/article/15d4e8230af3414ba595b2eb6c57f40e
Publikováno v:
Data, Vol 6, Iss 1, p 6 (2021)
Finding dependencies in the data requires the analysis of relations between dozens of parameters of the studied process and hundreds of possible sources of influence on this process. Dependencies are nondeterministic and therefore modeling requires t
Externí odkaz:
https://doaj.org/article/ac6679a0e3144e2086b24de6c53faf24
Autor:
Nataliia Melnykova, Nataliya Shakhovska, Michal Gregus, Volodymyr Melnykov, Mariana Zakharchuk, Olena Vovk
Publikováno v:
Mathematics, Vol 8, Iss 8, p 1211 (2020)
The study was conducted by applying machine learning and data mining methods to treatment personalization. This allows individual patient characteristics to be investigated. The personalization method was built on the clustering method and associativ
Externí odkaz:
https://doaj.org/article/b77b1d04bae34170bc75eeb11e215314
Publikováno v:
Computers, Materials & Continua. 70:3969-3984
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4af0127f10f559f1b8f17b38d7ba4406
https://doi.org/10.32604/cmc.2022.019882
https://doi.org/10.32604/cmc.2022.019882
A Novel Approach for the Automatic Detection of COVID in a Patient by Using a Categorization Methods
Autor:
Nataliia Melnykova
Publikováno v:
Procedia Computer Science. 198:712-717
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::13e65b925e72ebc2a416dc02c45d6f83
https://doi.org/10.1016/j.procs.2021.12.311
https://doi.org/10.1016/j.procs.2021.12.311
Autor:
Natalya Shakhovska, Nataliia Melnykova
Publikováno v:
Computer Modeling and Intelligent Systems. 3137:48-57
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::50573183c2a9961fb57622daeb0dee52
https://doi.org/10.32782/cmis/3137-4
https://doi.org/10.32782/cmis/3137-4
Autor:
Igor Yakovlev, Rostyslav Zhuk, Yaroslav Tolstyak, Nataliya Shakhovska, Valentyna Chopyak, Nataliia Melnykova, Michal Greguš ml.
Publikováno v:
Applied Sciences, Vol 11, Iss 10380, p 10380 (2021)
Applied Sciences
Volume 11
Issue 21
Applied Sciences
Volume 11
Issue 21
Machine learning is used to develop predictive models to diagnose different diseases, particularly kidney transplant survival prediction. The paper used the collected dataset of patients’ individual parameters to predict the critical risk factors a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f140248ad3ff60cf1bdfcae2d5b20a86
https://www.mdpi.com/2076-3417/11/21/10380
https://www.mdpi.com/2076-3417/11/21/10380
Autor:
Kateryna Melnykova, Khrystyna Lishchuk-Yakymovych, Nataliya Shakhovska, Nataliia Melnykova, Volodymyr Melnykov
Publikováno v:
Big Data and Cognitive Computing, Vol 5, Iss 37, p 37 (2021)
Big Data and Cognitive Computing
Volume 5
Issue 3
Big Data and Cognitive Computing
Volume 5
Issue 3
The paper describes the medical data personalization problem by determining the individual characteristics needed to predict the number of days a patient spends in a hospital. The mathematical problem of patient information analysis is formalized, wh
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::3ce25264516de31577f7e4ad22b3a364
https://www.mdpi.com/2504-2289/5/3/37
https://www.mdpi.com/2504-2289/5/3/37
Publikováno v:
Procedia Computer Science. 155:624-629
The article covers the formalization of time-dependent and time-independent data of various origins of the investigated object, which are necessary for personalization of patient’s data in the process of seeking an individual approach to choosing a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5a4d79696bd3c486ef58087422092e3
https://doi.org/10.1016/j.procs.2019.08.088
https://doi.org/10.1016/j.procs.2019.08.088