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.
CITATION STYLE
Wajs, W., Wojtowicz, H., Wais, P., & Ochab, M. (2017). Prediction of arterial blood gases values in premature infants with respiratory disorders. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10192 LNAI, pp. 434–444). Springer Verlag. https://doi.org/10.1007/978-3-319-54430-4_42
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