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Biologically Inspired Cognitive Architectures Meeting

BICA 2019: Biologically Inspired Cognitive Architectures 2019 pp 246–259Cite as

Dynamics of Recognition of Properties in Diagnostics

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 948))

Abstract

The issues of processing semantic information for diagnostic studies are considered, in which, with the advent of personalized medicine, the task of ensuring efficiency and accuracy has come to the fore. The solution of such problems is associated with the need to recognize the properties of the samples, which is done by experts/crowdsourcers using the means of semantic modeling. In this regard, the analysis is subject to communication (1) level of knowledge with (2) forms of information processing and with (3) levels of abstraction. For this, a dynamic semantic model is developed and applied that can take into account such parameterization.

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Acknowledgements

This research is supported in part by the Russian Foundation for Basic Research, RFBR grants 19-07-00326-a, 19-07-00420-a, 18-07-01082-a, 17-07-00893-a.

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Authors and Affiliations

  1. National Research Nuclear University “Moscow Engineering Physics Institute”, Moscow, 115409, Russian Federation

    Larisa Ismailova & Viacheslav Wolfengagen

  2. Institute for Contemporary Education, “JurInfoR-MGU”, Moscow, 119435, Russian Federation

    Sergey Kosikov

Authors
  1. Sergey Kosikov
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  2. Larisa Ismailova
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  3. Viacheslav Wolfengagen
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Corresponding author

Correspondence to Viacheslav Wolfengagen .

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Editors and Affiliations

  1. Moscow Engineering Physics Institute (MEPhI), Department of Cybernetics, National Research Nuclear University (NRNU), Moscow, Russia

    Alexei V. Samsonovich

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Kosikov, S., Ismailova, L., Wolfengagen, V. (2020). Dynamics of Recognition of Properties in Diagnostics. In: Samsonovich, A. (eds) Biologically Inspired Cognitive Architectures 2019. BICA 2019. Advances in Intelligent Systems and Computing, vol 948. Springer, Cham. https://doi.org/10.1007/978-3-030-25719-4_32

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