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Tytuł artykułu

Neural network and artificial immune algorithms for the classification of medical data series

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Warianty tytułu
PL
Sieci neuronowe i sieci immunologiczne dla rozpoznawania przypadków medycznych
Języki publikacji
EN
Abstrakty
EN
This paper describes the applicability of artificial immune algorithms. Medical data series classification technique by Artificial Immune Algorithm is used for Neural Network Algorithm input data definitions. Artificial Immune Algorithms is created and trained for the purpose of Arterial Blood Gas parameters classification: pH, PaCO2, PaO2, HCO3. The main goal of this paper is to develop a artificial neural network technique for Arterial Blood Gases short-term prediction. The main question that is considered is how to predict some dynamic parameters that describe blood gases nature. A model of a physical system has an error associated with its predictions due to the dependences of the physical system's output on uncontrollable and unobservable quantities. The use of artificial methods creates the possibilities of obtaining some parameter values on the proper level of probability. This would provide a direct feedback to the clinical staff about the progress of a patient, the success of individual treatments, and quality of care as well as predicting blood gas value.
PL
Dla rozpoznawania przypadków chorobowych, które są opisane numerycznymi danymi wykorzystano metody sztucznej inteligencji. W pracy wykorzystano dwie metody: metodę sztucznych sieci neuronowych oraz metodę sztucznych sieci immunologicznych. Przedstawiono wyniki uzyskane tymi metodami w odniesieniu do przypadków dysplazji oskrzelowo płucnej dla dzieci, których waga była poniżej 1500 g.
Wydawca
Rocznik
Strony
89--96
Opis fizyczny
Bibliogr. 15 poz., rys., wykr., tab.
Twórcy
autor
  • AGH University of Science and Technology, Krakow, Poland
Bibliografia
  • [1] De Castro L.N., Von Zuben F.J., An Evolutionary Immune Network for Data Clustering. Proc. of the IEEE SBRN, 2000, 84-89.
  • [2] De Castro L.N., Von Zuben F. J., The Clonal Selection Algorithm with Engineering Applications. GECCO'00 Workshop Proceedings, 2000, 36-37.
  • [3] De Castro L.N., Von Zuben F.J., Artificial Immune Systems. Part I - Basic Theory and Applications, Technical Report - RTDCA 01/99, 1999.
  • [4] Wajs W., Predicting of Dynamic Medical Data Series Using Neural Network Method. 15thTriennial World Congress of the IFAC b'02 Barcelona 2002.
  • [5] Jerne N.K., Towards a Network Theory of the Immune System. Ann. Immue nol. Ins. Pasteur, 125C, 1974, 373-389.
  • [6] Jerne N.K., Clonal Selection in a Lymphocye Network. In G.M. Edelman (Ed.). Cellular Selection and Regulation in the Immune Response (p. 39). Raven Press, New York 1974.
  • [7] Leclerc B., Minimum Spanning Tree for Tree Matrices, Abridgements and Adjustments. Journal of Classification, 12, 1995, 207-241.
  • [8] Zahn C.T., Graph-theoretical Methods for Detecting and Describing Gestalt Clusters. IEEE Transactins on Computers, C-20(l), 1971, 68-86.
  • [9] Varela F.J., Coutinho A., Second Generation Immune Networks. Immunology Today, 12(5), 1991, 159-166.
  • [10] Chryssolouris G.C., Lee M., Ramsey A., Confidence Intemal Prediction for Neural Network Models. IEEE Trans, on Neural Networks, vol. 7, No 1, Jan. 1996.
  • [11] Graves S.C., Redfield C.H., Eąuipment selection and task assignment for multi product assembly system design. Int. J. Flexib. Manufacturing Sys., vol. 1, 1988, 31-50.
  • [12] Lippman R.P., An introduction to computing with neural nets. IEEE ASSP Magazine, 1987, 2-22.
  • [13] Bonna C.A., Kohler H., Immune Networks. Annales of the New York Academy of Sciences, 418, 1983.
  • [14] Carrol J.D., Minimax Length Links' of a Dissimilarity Matrix and Minimum Spanning Trees. Psychometrica, 60(3), 1995, 371-374.
  • [15] Lapointe F-J., Legendre P., The Generation Tests for Dendrograms: A Comparative Evaluation. Journal of Classification, 8, 1991, 177-200.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-article-AGH1-0032-0044
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