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.
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Antoniou, A., Lu, W.: Practical Optimization: Algorithms and Engineering Applications. Springer (2007)
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)
Davidon, W.C.: Variable Metric Method for Minimization. A.E.C. Research and Development Report, ANL-5990 (1959)
Fletcher, R.: A new approach to variable metric algorithms. Comput. J. 13, 317–322 (1970)
Fletcher, R.: Practical Methods of Optimization. Wiley (1987)
Fletcher, R., Powell, M.J.D.: A rapidly convergent descent method for minimization. Comput. J. 6, 163–168 (1963)
Kelley, C.T.: Iterative Methods for Optimization. North Carolina State University, SIAM (1999)
Kofstad, J.: Blood gases and hypothermia: some theoretical and practical considerations. Scand. J. Clin. Lab Invest. (Suppl) 224, 21–26 (1996) (PMID 8865418)
Levenberg, K.: A method for the solution of certain problems in least squares. Quart. Appl. Math. 2, 164–168 (1944)
Lippman, R.P.: An introduction to computing with neural nets. IEEE ASSP Mag. 4–22 (1987)
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)
Marquardt, D.: An algorithm for least-squares estimation of nonlinear parameters. SIAM J. Appl. Math. 11, 431–441 (1963)
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)
Tadeusiewicz, R.: Neural Network as a tool for modeling of biological systems. Bio-Algor. Med. Syst. 11(3), 135–144 (2015)
Transtrum, M.K., Machta, B.B., Sethna, J.P.: Why are nonlinear fits to data so challenging? Phys. Rev. Lett. 104, 060201 (2010)
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Wajs, W., Wais, P., Ochab, M., Wojtowicz, H. (2016). Arterial Blood Gases Forecast Optimization by Artificial Neural Network Method. In: Piętka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technologies in Medicine. ITiB 2016. Advances in Intelligent Systems and Computing, vol 471. Springer, Cham. https://doi.org/10.1007/978-3-319-39796-2_36
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DOI: https://doi.org/10.1007/978-3-319-39796-2_36
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