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Prediction of Arterial Blood Gases Values in Premature Infants with Respiratory Disorders

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Intelligent Information and Database Systems (ACIIDS 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10192))

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Abstract

Arterial blood gases sampling (ABG) is a method for acquiring neonatal patients’ acid-base status. Variations of blood gasometry parameters values over time can be modelled using multi-layer artificial neural networks (ANNs). Accurate predictions of future levels of blood gases can be useful in supporting therapeutic decision making. In the paper several models of ANN are trained using growing numbers of feature vectors and assessment is made about the influence of input matrix size on the accuracy of ANNs’ prediction capabilities.

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Author information

Authors and Affiliations

  1. The University of Rzeszów, 16c Al. Rejtana, 35-959, Rzeszów, Poland

    Wiesław Wajs & Hubert Wojtowicz

  2. State Higher Vocational School in Krosno, Rynek 1, 38-400, Krosno, Poland

    Piotr Wais

  3. AGH University of Science and Technology, 30 Mickiewicza, 30-059, Kraków, Poland

    Marcin Ochab

Authors
  1. Wiesław Wajs
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  2. Hubert Wojtowicz
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  3. Piotr Wais
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  4. Marcin Ochab
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Corresponding author

Correspondence to Hubert Wojtowicz .

Editor information

Editors and Affiliations

  1. Wrocław University of Science and Technology, Wroclaw, Poland

    Ngoc Thanh Nguyen

  2. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Satoshi Tojo

  3. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Le Minh Nguyen

  4. Wrocław University of Science and Technology, Wroclaw, Poland

    Bogdan Trawiński

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Wajs, W., Wojtowicz, H., Wais, P., Ochab, M. (2017). Prediction of Arterial Blood Gases Values in Premature Infants with Respiratory Disorders. In: Nguyen, N., Tojo, S., Nguyen, L., Trawiński, B. (eds) Intelligent Information and Database Systems. ACIIDS 2017. Lecture Notes in Computer Science(), vol 10192. Springer, Cham. https://doi.org/10.1007/978-3-319-54430-4_42

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  • DOI: https://doi.org/10.1007/978-3-319-54430-4_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-54429-8

  • Online ISBN: 978-3-319-54430-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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