Artificial intelligence: A new approach for prescription and monitoring ofhemodialysis therapy

The effect of dialysis on patients is conventionally predicted using a form al mathematical model. This approach requires many assumptions of the proce sses involved, and validation of these may be difficult. The validity of di alysis urea modeling using a formal mathematical model has been challenged. Artificial intelligence using neural networks (NNs) has been used to solve complex problems without needing a mathematical model or an understanding of the mechanisms involved. In this study, we applied an NN model to study and predict concentrations of urea during a hemodialysis session. We measur ed blood concentrations of urea, patient weight, and total urea removal by direct dialysate quantification (DDQ) at 30-minute intervals during the ses sion (in 15 chronic hemodialysis patients). The NN model was trained to rec ognize the evolution of measured urea concentrations and was subsequently a ble to predict hemodialysis session time needed to reach a target solute re moval index (SRI) in patients not previously studied by the NN model (in an other 15 chronic hemodialysis patients). Comparing results of the NN model with the DDQ model, the prediction error was 10.9%, with a not significant difference between predicted total urea nitrogen (UN) removal and measured UN removal by DDQ. NN model predictions of time showed a not significant di fference with actual intervals needed to reach the same SRI level at the sa me patient conditions, except for the prediction of SRI at the first 30-min ute interval, which showed a significant difference (P = 0.001). This indic ates the sensitivity of the NN model to what Is called patient clearance ti me; the prediction error was 8.3%. From our results, we conclude that artif icial intelligence applications in urea kinetics can give an idea of intrad ialysis profiling according to individual clinical needs. In theory, this a pproach can be extended easily to other solutes, making the NN model a step forward to achieving artificial-intelligent dialysis control. (C) 2001 by the National Kidney Foundation, Inc.