Modeling of urea concentration in serum after hemodialysis

• Autorzy: Nowak P. , Szczepaniak P. S. , Filutowicz J. , Ojha P. C.
• Języki publikacji: EN

Abstrakty

EN

The paper presents a general review of two approaches to modeling of urea concentration in serum after hemodialysis. The first approach is a well-established classic method, widely accepted by medical staff dealing with practical nephrology. The other one utilises the power of artificial neural networks, which is a novel application to that field. Unlike classic models that base on theoretical investigation, this technique uses only a number of data from previous treatment to construct a model through so called learning from examples.

Słowa kluczowe

EN

hemodialysis; urea kinetic modelling; urea rebound; neural networks

PL

hemodializa; modelowanie kinetyczne mocznika; odbicie mocznika; sieci neuronowe

• Wydawca: University of Silesia, Institute of Informatics, Computer Systems Department
• Czasopismo: Journal of Medical Informatics & Technologies
• Rocznik: 2001
• Tom: Vol. 2, Part. 2
• Strony: SN9--SN14
• Opis fizyczny: Bibliogr. 11 poz.

Twórcy

autor

Nowak P.

• Institute of Computer Science, Technical University of Lodz, Poland

autor

Szczepaniak P. S.

• Institute of Computer Science, Technical University of Lodz, Poland
• Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

autor

Filutowicz J.

• Dialysis Centre, N. Copernicus Regional Joint Hospital in Lodz, Poland

autor

Ojha P. C.

• School of Information and Software Engineering, University of Ulster at Jordanstown, Northern Ireland, UK

Bibliografia

• [1] DAUGIRDAS J., SCHNEDITZ D., Overestimation of Hemodialysis Dose Depends on Dialysis Efficiency by Regional Blood Flow but not by Conventional Two-Pool Urea Kinetic Analysis. ASAIO J, 41, M719-M724, 1995.
• [2] GOTCH F., A Quantitative Evaluation of Small and Middle Molecule Toxicity in Therapy of Uremia. Dial. Transplant, 9, 1980.
• [3] GUH JY, YANG CY, YANG JM, CHEN LM, LAI YH, Prediction of Equilibrated Postdialysis BUN by an Artificial Neural Network in High-Efficiency Hemodialysis. American Journal of Kidney Diseases, Vol. 31, No 4, pp. 638-646, 1998.
• [4] HAYKIN S., Neural Networks. A Comprehensive Foundation. MacMilan Publ. Company, New York, 1994.
• [5] LOPOT F., Urea Kinetic Modelling. EDTNA-ERCA Series, Vol. 4, pp. 17-48, 1990.
• [6] PIETRZYK J., Ocena skuteczności dializy u dzieci na podstawie parametrów modelowania kinetycznego mocznika (Dialysis Efficiency Evaluation in Children Based on UKM Parameters). Jagiellonian University, Collegium Medicum, pp. 7-28, 68-71, Cracow, 1994 (in Polish).
• [7] SMYE S., DUNDERDALE E., BROWNRIDGE G., WILL E., Estimation of Treatment Dose in High-Efficiency Haemodialysis. Nephron 67, pp.24-29, 1994.
• [8] SZCZEPANIAK P., FILUTOWICZ J., NOWAK P., OJHA P., Classic and Neural Urea Kinetic Modelling. Proc. 6th Int. Conf. SYMBIOSIS'2001, Szczyrk, 2001.
• [9] SZCZEPANIAK P., NOWAK P., FILUTOWICZ J., OJHA P., Analysis of Input Significance for Neural Urea Kinetic Modelling. (to be published), 2001.
• [10] TADEUSIEWICZ R., Sieci neuronowe (Neural Networks). Akademicka Oficyna Wydawnicza RM, Warsaw, 1993 (in Polish).
• [11] TATTERSAL J. et al., The Post-Hemodialysis Rebound: Predicting and Quantifying its Effect on Kt/V. Kidney International, Vol. 50, pp. 2094-2102,1996.