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

  • Wiesław Wajs
  • Hubert WojtowiczEmail author
  • Piotr Wais
  • Marcin Ochab
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10192)

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.

Keywords

Neural Network Model Base Excess Gradient Descent Algorithm Transcutaneous Carbon Dioxide Metabolism Rate Constant 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Wiesław Wajs
    • 1
  • Hubert Wojtowicz
    • 1
    Email author
  • Piotr Wais
    • 2
  • Marcin Ochab
    • 3
  1. 1.The University of RzeszówRzeszówPoland
  2. 2.State Higher Vocational School in KrosnoKrosnoPoland
  3. 3.AGH University of Science and TechnologyKrakówPoland

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