Intelligent systems. Theory and applications
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Intelligent systems. Theory and applications, 2021, Volume 25, Issue 2, Pages 23–45 (Mi ista301)  

This article is cited in 1 scientific paper (total in 1 paper)

Part 1. General problems of the intellectual systems theory

The adverse clinical outcome risk assessment by in-depth data analysis methods

B. E. Gornyia, A. P. Ryjovb, A. S. Strogalovb, A. D. Zhuravlevb, A. A. Khusaenovb, I. A. Sherginb, D. A. Feshchenkoa, A. M. Abdullaeva, A. V. Kontsevayaa

a National Medical Research Center for Therapy and Preventive Medicine
b Moscow State University
Full-text PDF (920 kB) Citations (1)
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Abstract: The adverse events in the medical care providing process occurs in 10-15% of hospitalized patients. Even a few percent reducing of such events will save thousands of lives. One of the ways to solve this crucial problem is the usage of intelligent information technologies that allow the predicting of an unfavorable clinical outcome risk in patients. The paper presents the study results carried out jointly by the scientist of the National Research Center for Therapy and Preventive Medicine of the Ministry of Health of the Russian Federation and the the scientist of Faculty of Mechanics and Mathematics of the Lomonosov Moscow State University, showing the applicability of data analysis methods in this important problem solving.
Keywords: preventive medicine, adverse clinical outcome, in-depth data analysis.
Document Type: Article
Language: Russian
Citation: B. E. Gornyi, A. P. Ryjov, A. S. Strogalov, A. D. Zhuravlev, A. A. Khusaenov, I. A. Shergin, D. A. Feshchenko, A. M. Abdullaev, A. V. Kontsevaya, “The adverse clinical outcome risk assessment by in-depth data analysis methods”, Intelligent systems. Theory and applications, 25:2 (2021), 23–45
Citation in format AMSBIB
\Bibitem{GorRyjStr21}
\by B.~E.~Gornyi, A.~P.~Ryjov, A.~S.~Strogalov, A.~D.~Zhuravlev, A.~A.~Khusaenov, I.~A.~Shergin, D.~A.~Feshchenko, A.~M.~Abdullaev, A.~V.~Kontsevaya
\paper The adverse clinical outcome risk assessment by in-depth data analysis methods
\jour Intelligent systems. Theory and applications
\yr 2021
\vol 25
\issue 2
\pages 23--45
\mathnet{http://mi.mathnet.ru/ista301}
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  • https://www.mathnet.ru/eng/ista301
  • https://www.mathnet.ru/eng/ista/v25/i2/p23
  • This publication is cited in the following 1 articles:
    Citing articles in Google Scholar: Russian citations, English citations
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    Intelligent systems. Theory and applications
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    Abstract page:114
    Full-text PDF :56
    References:17
     
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