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Russian Conference on Artificial Intelligence

RCAI 2021: Artificial Intelligence pp 289–302Cite as

Knowledge-Based Diagnostic System With a Precedent Library

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 12948))

Abstract

The hypothesis of the presumptive diagnosis before laboratory confirmation is especially important in orphan (rare) hereditary diseases. It is possible to solve this problem using computer-based decision support systems based on knowledge. However, in medical practice, there are cases of an atypical clinical picture in patients with fuzzy manifestations of features. In such cases, it is possible to increase the diagnostic accuracy using a precedent approach. The concept of “synthetic precedent” is introduced, which is the result of the transformation of an atypical case into a synthesized description. The paper presents methods for constructing synthetic precedents of two types. The precedents of the first type are created as a result of extension with the fuzzy boundaries for ordinal variables. The precedents of the second type are received by softening the requirement for the number of necessary signs of a patient to match an atypical case from the precedent library. An approach to the creation of a hybrid system, including a traditional knowledge base and a precedent library, is proposed and demonstrated. The use of the hybrid system increases the accuracy of early diagnosis of orphan diseases in childhood.

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Acknowledgements

This work was carried out with partial financial support from the Russian Foundation for Basic Research RFBR (project No. 19–29-01077, 18–29-03131).

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Authors and Affiliations

  1. Federal Research Center “Computer Science and Control” of the Russian Academy of Sciences, Moscow, Russian Federation

    Nikolay Blagosklonov & Boris Kobrinskii

  2. Institute of Automation and Control Processes of the Far Eastern Branch of the Russian Academy of Sciences, Vladivostok, Russian Federation

    Valeriya Gribova & Elena Shalfeeva

Authors
  1. Nikolay Blagosklonov
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  2. Valeriya Gribova
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  3. Boris Kobrinskii
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  4. Elena Shalfeeva
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Corresponding author

Correspondence to Nikolay Blagosklonov .

Editor information

Editors and Affiliations

  1. Rostov State Transport University, Rostov-on-Don, Russia

    Sergei M. Kovalev

  2. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  3. Russian Academy of Sciences, Moscow, Russia

    Aleksandr I. Panov

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Blagosklonov, N., Gribova, V., Kobrinskii, B., Shalfeeva, E. (2021). Knowledge-Based Diagnostic System With a Precedent Library. In: Kovalev, S.M., Kuznetsov, S.O., Panov, A.I. (eds) Artificial Intelligence. RCAI 2021. Lecture Notes in Computer Science(), vol 12948. Springer, Cham. https://doi.org/10.1007/978-3-030-86855-0_20

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  • DOI: https://doi.org/10.1007/978-3-030-86855-0_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86854-3

  • Online ISBN: 978-3-030-86855-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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