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Possibilities of Neural Networks for Personalization Approaches for Prevention of Complications After Endovascular Interventions

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Book cover Advances in Neural Networks - ISNN 2017 (ISNN 2017)

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Abstract

It is known that most of the diseases of the cardiovascular system are accompanied by disorders in the hemostatic system. The hemostatic system is one of the most complex systems. It has a hierarchical structure with a plurality of components. We analyze the results of thrombin generation test (TGT) which allows of estimating the actions of all components of the hemostatic system. The problem is complicated by the presence of too many various clinical cases. The simple statistical methods do not provide global assessments. We suggest the universal neural network approach for building hemostatic system models based on the factors which don’t have a statistically significant difference for various types of clinical post surgery cases. The neural network instruments allow of taking into account the nonlinear hierarchical nature of considered system and building individual models for each clinical cases. The aim of our study is to develop the neural network hemostatic system model for forecasting of disease progression and complications after endovascular interventions.

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Acknowledgements

The work was supported by the Russian Foundation for Basic Research, project number 14-38-00009.

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Authors and Affiliations

  1. CC FEB RAS, 65, Kim Yu Chen Street, 680000, Khabarovsk, Russia

    Tatiana V. Lazovskaya

  2. Peter the Great St. Petersburg Polytechnical University, 29 Politechnicheskaya str., 195251, Saint Petersburg, Russia

    Dmitriy A. Tarkhov

  3. Federal Almazov Medical Research Centre, 2 Akkuratova str., 197341, Saint Petersburg, Russia

    Gelena A. Berezovskaya & Nikolay N. Petrischev

  4. Pavlov First Saint Petersburg State Medical University, 6-8 L’va Tolstogo str., 197022, Saint Petersburg, Russia

    Gelena A. Berezovskaya & Nikolay N. Petrischev

  5. Bashkir State University, 32 Zaki Validi str., 450076, Ufa, Russia

    Ildar U. Zulkarnay

Authors
  1. Tatiana V. Lazovskaya
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  2. Dmitriy A. Tarkhov
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  3. Gelena A. Berezovskaya
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  4. Nikolay N. Petrischev
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  5. Ildar U. Zulkarnay
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Corresponding author

Correspondence to Tatiana V. Lazovskaya .

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Editors and Affiliations

  1. Dalian University of Technology, Dalian, China

    Fengyu Cong

  2. City University of Hong Kong, Kowloon Tong, Hong Kong

    Andrew Leung

  3. Chinese Academy of Sciences, Beijing, China

    Qinglai Wei

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Lazovskaya, T.V., Tarkhov, D.A., Berezovskaya, G.A., Petrischev, N.N., Zulkarnay, I.U. (2017). Possibilities of Neural Networks for Personalization Approaches for Prevention of Complications After Endovascular Interventions. In: Cong, F., Leung, A., Wei, Q. (eds) Advances in Neural Networks - ISNN 2017. ISNN 2017. Lecture Notes in Computer Science(), vol 10261. Springer, Cham. https://doi.org/10.1007/978-3-319-59072-1_45

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  • DOI: https://doi.org/10.1007/978-3-319-59072-1_45

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59071-4

  • Online ISBN: 978-3-319-59072-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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