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Predictive Analysis of the pO2 Blood Gasometry Parameter Related to the Infants Respiration Insufficiency

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Intelligent Information Processing and Web Mining

Part of the book series: Advances in Soft Computing ((AINSC,volume 35))

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Abstract

The article presents application of artificial immune algorithms in prediction of the pO2 arterial blood gasometry parameter, which is related to the infants respiration insufficiency. Artificial immune network algorithm created for this purpose allows for time series prediction of the vectorized data sets. Training data originates from the Infant Intensive Care Unit of the Polish – American Institute of Pediatry, Collegium Medicum, Jagiellonian University in Cracow.

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References

  1. 1. Kruczek, P. Assesment of neural networks methods usefulness in prediction of premature neonates respiration insu.ciency, Doctoral Dissertation, Collegium Medicum, Jagiellonian University in Cracow, Cracow 2001.

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Wajs, W., Swiecicki, M., Wais, P., Wojtowicz, H., Janik, P., Nowak, L. (2006). Predictive Analysis of the pO2 Blood Gasometry Parameter Related to the Infants Respiration Insufficiency. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 35. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-33521-8_40

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  • DOI: https://doi.org/10.1007/3-540-33521-8_40

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-33520-7

  • Online ISBN: 978-3-540-33521-4

  • eBook Packages: EngineeringEngineering (R0)

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