Abstract
Artificial Neural Networks are mathematical models resembling the brain behavior. They have the ability to “learn” from the “environment” and produce responses as a consequence of this learning process. They were broadly used in medicine both as a classification model as well as a prediction tool. In hemodialysis they were used for molecular modeling in the estimation of equilibrated urea concentration, as a monitoring strategy for online treatment analysis and also for bed side models for hemodialysis adequacy evaluation. In this chapter the basic concepts of artificial neural models are introduced and a complete application in equilibrated urea estimation in hemodialized patients is presented.
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References
Alloati, S., Molino, A., Manes, M., Bosticardo, G.M.: Urea rebound and effectively delivered dialysis dose. Nephrol. Dial. Transplant. 13(6), 25–30 (1998)
Azar, A.T., Wahba, K.M.: Artificial Neural Network for Prediction of Equilibrated Dialysis Dose without Intradialytic Sample. Saudi J. Kidney Dis. Transpl. 22(4), 705–711 (2011)
Azar, A.T., Balas, V.E., Olariu, T.: Artificial Neural Network for Accurate Prediction of Post-Dialysis Urea Rebound (2010), doi: 10.1109/SOFA.2010.5565606
Bhaskaran, S., Tobe, S., Saiphoo, C., et al.: Blood urea levels 30 minutes before the end of dialysis are equivalent to equilibrated blood urea. ASAIO J. 43(5), M759–M762 (1997)
Barro, S., Mira, J.: Computación Neuronal. Servicio de Publica-ciones de la. Universidad de Santiago de Compostela (1995)
Beale, M.H., Hagan, M.T., Demuth, H.B.: Neural Network ToolboxTM 7 User’s Guide (September 2010), http://www.mathworks.com/help/pdf_doc/nnet/nnet.pdf
Bland, J.M., Altman, D.G.: Statistical methods for assessing agreement between two methods of clinical measurement. Lancet 8, 307–310 (1986)
Canaud, B., Bosc, J.Y., Leblanc, M., et al.: A simple and accurate method to determine equilibrated post-dialysis urea concentration. Kidney Int. 51(6), 2000–2005 (1997)
Chiu, J., Chong, C., Lin, Y., Wu, C., Wang, Y., Li, Y.: Applying and Artificial Neural Network to predict Total Body Water in Hemodialysis patients. Am. J. Nephrol. 25(5), 507–513 (2005), doi:10.1159/000088279
Daugirdas, J.T.: Second generation logarithmic estimates of single-pool variable volume Kt/V: an analysis of error. J. Am. Soc. Nephrol. 4(5), 1205–1213 (1993)
Daugirdas, J.T.: Simplified equations for monitoring Kt/V, PCRn, eKt/V, and ePCRn. Adv. Ren. Replace. Ther. 2(4), 295–304 (1995)
Daugirdas, J.T., 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(3), M719–M724 (1995)
Daugirdas, J.T., Depner, T.A., Gotch, F.A., et al.: Comparison of methods to predict equilibrated Kt/V in the HEMO Pilot Study. Kidney Int. 52(5), 1395–1404 (1997)
Drachman, D.: Do we have brain to spare? Neurology 64(12), 2004–2005 (2005), doi:10.1212/01.WNL.0000166914.38327.BB
Fernandez, E.A., Valtuille, R., Willshaw, P., Perazzo, C.A.: Using Artificial Intelligence to Predict the Equilibrated Post-dialysis. Blood Urea Concentration Blood Purif. 19(3), 271–285 (2001)
Fernandez, E.A., Valtuille, R., Willshaw, P., Perazzo, C.A.: Dialysate-side Urea Kinetics. Neural Network Predicts Dialysis Dose During Dialysis. Med. Biol. Eng. Comput. 41(4), 392–396 (2003)
Fernandez, E.A., Valtuille, R., Presedo, J., Willshaw, P.: Comparison of different methods for hemodialysis evaluation by means of ROC curves: from artificial intelligence to current methods. Clinical Nephrology 64(3), 205–213 (2005a)
Fernandez, E.A., Valtuille, R., Presedo, J., et al.: Comparison of standard and artificial neural network estimators of hemodialysis adequacy. Artificial Organs 29(2), 159–165 (2005b)
Gabutti, L., Vadilonga, D., Mombelli, G., Burnier, M., Marone, C.: Artificial neural networks improve the prediction of Kt/V, follow-up dietary protein intake and hypotension risk in haemodialysis patients. Nephrol Dial Transplant. 19(5), 1204–1211 (2004a)
Gabutti, L., Burnier, M., Mombelli, G., Malé, F., Pellegrini, L., Marone, C.: Usefulness of artificial neural networks to predict follow-up dietary protein intake in hemodialysis patients. Kidney Int. 66(1), 399–407 (2004b)
Gotch, F., Sargent, A.: A mechanistic analysis of the National Cooperative Dialysis Study. Kidney Int. 28(3), 526–534 (1985)
Guh, J., Yang, C., Yang, J., Chen, L., Lai, Y.: Prediction of equilibrated postdialysis BUN by an artificial neural network in high-efficiency hemodialysis. Am. J. Kidney. Dis. 31(4), 638–646 (1998)
Hagan, M., Menhaj, M.: Training feed-forward networks with the Marquardt algorithm. IEEE Trans. on Neural. Netw. 5, 989–993 (1994)
Haykin, S.: Neural Networks. A Comprehensive Foundation, 2nd edn. Prentice Hall, USA (1999)
Hopfield, J.J.: Neural Networks and Physical Systems with Emergent Collective Computational Abilities. Proceedings of the National Academy of Sciences of the USA 79(8), 2554–2558 (1982)
Kaufman, A.M., Schneditz, D., Smye, S.W., et al.: Solute disequlibrium and multicompartment modeling. Adv. Ren. Replac. Ther. 2(4), 319–329 (1995)
Kosko, B.: Neural Networks and Fuzzy Systems. Prentice Hall, USA (1992)
Kohonen, T.: Self-Organizing Maps, 2nd edn. Springer, Heidelberg (1997)
Lowrie, E.G., Lew, N.L.: The urea reducton ratio (URR): a simple method for evaluating haemodialysis treatment. Contemp. Dial. Nephrol. 12, 11–20 (1991)
Maduell, F., Garcia-Valdecasas, J., et al.: Validation of different methods to calculate Kt/V considering post-dialysis rebound. Nephrol. Dial. Transplant. 12(9), 1928–1933 (1997)
Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Rumelhart, D.E., McClelland, J.L. (eds.) Parallel Distributed Processing: Explorations in the Microstructure of Cognition, vol. 1, MIT Press, Cambridge (1986)
Smye, S.W., Evans, J.H., Will, E., Brocklebank, J.T.: Paediatric haemodialysis: Estimation of treatment efficiency in the presence of urea rebound. Clin. Phys. Physiol. Meas. 13(1), 51–62 (1992)
Smye, S.W., Hydon, P.E., Will, E.: An Analysis of the Single-Pool Urea Kinetic Model and Estimation of Errors. Phys. Med. Biol. 38(1), 115–122 (1993)
Smye, S.W., Dunderdale, E., Brownridgr, G., Will, E.: Estimation of treatment dose in high-efficiency hemodialysis. Nephron 67(1), 24–29 (1994)
Tattersall, J.E., DeTakats, D., Chamney, P., et al.: The post dialysis rebound: predicting and quantifying its effect on Kt/V. Kidney Int. 50(6), 2094–2102 (1996)
National Kidney Foundation (2000), http://www.nkf.org
NKF-DOQI guidelines, http://www.kidney.org
European Best Practice Guidelines for Hemodialysis (EBPGH), http://www.ndt-educational.org/guidelines.asp
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Fernández, E.A., Valtuille, R., Balzarini, M. (2013). Artificial Neural Networks Applications in Dialysis. In: Azar, A. (eds) Modeling and Control of Dialysis Systems. Studies in Computational Intelligence, vol 405. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-27558-6_8
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DOI: https://doi.org/10.1007/978-3-642-27558-6_8
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