Advertisement

Arterial Blood Gases Forecast Optimization by Artificial Neural Network Method

  • Wiesław Wajs
  • Piotr Wais
  • Marcin Ochab
  • Hubert WojtowiczEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 471)

Abstract

Arterial blood gas sampling represents the gold standard method for acquiring patients’ acid-base status. It is proposed that blood gas values could be measured using arterialized earlobe blood samples. Pulse oximetry plus transcutaneous carbon dioxide measurement is an alternative method of obtaining similar information as well. Since dynamics of biochemical changes occurring in the blood is an individual feature which changes during the healing process authors proposed forecast models developed using artificial neural networks. The networks are trained with data vectors containing short term (72 h) history windows of four blood gasometry parameters. Several different optimization algorithms are used in the training phase to create a set of models from which the best prediction model is then selected.

Keywords

Arterial blood gas Forecast Artificial neural networks 

References

  1. 1.
    Antoniou, A., Lu, W.: Practical Optimization: Algorithms and Engineering Applications. Springer (2007)Google Scholar
  2. 2.
    Aaron, S.D., Vandemheen, K.L., Naftel, S.A., Lewis, M.J., Rodger, M.A.: Topical tetracaine prior to arterial puncture: a randomized, placebo-controlled clinical trial. Respir Med. 97(11), 1195–1199 (2003) (PMID 14635973)Google Scholar
  3. 3.
    Davidon, W.C.: Variable Metric Method for Minimization. A.E.C. Research and Development Report, ANL-5990 (1959)Google Scholar
  4. 4.
    Fletcher, R.: A new approach to variable metric algorithms. Comput. J. 13, 317–322 (1970)CrossRefzbMATHGoogle Scholar
  5. 5.
    Fletcher, R.: Practical Methods of Optimization. Wiley (1987)Google Scholar
  6. 6.
    Fletcher, R., Powell, M.J.D.: A rapidly convergent descent method for minimization. Comput. J. 6, 163–168 (1963)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Kelley, C.T.: Iterative Methods for Optimization. North Carolina State University, SIAM (1999)Google Scholar
  8. 8.
    Kofstad, J.: Blood gases and hypothermia: some theoretical and practical considerations. Scand. J. Clin. Lab Invest. (Suppl) 224, 21–26 (1996) (PMID 8865418)Google Scholar
  9. 9.
    Levenberg, K.: A method for the solution of certain problems in least squares. Quart. Appl. Math. 2, 164–168 (1944)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Lippman, R.P.: An introduction to computing with neural nets. IEEE ASSP Mag. 4–22 (1987)Google Scholar
  11. 11.
    Lourakis, M.I.A.: A brief description of the Levenberg-Marquardt algorithm implemented by levmar. Technical Report, Institute of Computer Science, Foundation for Research and Technology, Hellas (2005)Google Scholar
  12. 12.
    Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431–441 (1963)MathSciNetCrossRefzbMATHGoogle Scholar
  13. 13.
    Raoufy, M.R., Eftekhari, P., Gharibzadeh, S., Masjedi, M.R.: Predicting arterial blood gas values from venous samples in patients with acute exacerbation chronic obstructive pulmonary disease using artificial neural network. J. Med. Syst. 35(4), 483–488 (2011)CrossRefGoogle Scholar
  14. 14.
    Tadeusiewicz, R.: Neural Network as a tool for modeling of biological systems. Bio-Algor. Med. Syst. 11(3), 135–144 (2015)Google Scholar
  15. 15.
    Transtrum, M.K., Machta, B.B., Sethna, J.P.: Why are nonlinear fits to data so challenging? Phys. Rev. Lett. 104, 060201 (2010)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

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

Personalised recommendations