title: Artificial intelligence: a new approach for prescription and monitoring of hemodialysis therapy.
abstract: The effect of dialysis on patients is conventionally predicted using a formal mathematical model. This approach requires many assumptions of the processes involved, and validation of these may be difficult. The validity of dialysis 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 measured blood concentrations of urea, patient weight, and total urea removal by direct dialysate quantification (DDQ) at 30-minute intervals during the session (in 15 chronic hemodialysis patients). The NN model was trained to recognize the evolution of measured urea concentrations and was subsequently able to predict hemodialysis session time needed to reach a target solute removal index (SRI) in patients not previously studied by the NN model (in another 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 difference with actual intervals needed to reach the same SRI level at the same patient conditions, except for the prediction of SRI at the first 30-minute interval, which showed a significant difference (P = 0.001). This indicates the sensitivity of the NN model to what is called patient clearance time; the prediction error was 8.3%. From our results, we conclude that artificial intelligence applications in urea kinetics can give an idea of intradialysis profiling according to individual clinical needs. In theory, this approach can be extended easily to other solutes, making the NN model a step forward to achieving artificial-intelligent dialysis control.
authors.affiliation: Urology and Nephrology Center and Control and Computer Science, Mansoura University, Mansoura, Egypt. aiakl2001@yahoo.com
authors.forename: A I
authors.initials: AI
authors.lastname: Akl
authors.forename: M A
authors.initials: MA
authors.lastname: Sobh
authors.forename: Y M
authors.initials: YM
authors.lastname: Enab
authors.forename: J
authors.initials: J
authors.lastname: Tattersall
mesh_terms: D000328:Adult; D000704:Analysis of Variance; D001806:Blood Urea Nitrogen; D005260:Female; D006801:Humans; D008297:Male; D008954:Models, Biological; D008991:Monitoring, Physiologic / Q000379:methods; D016571:Neural Networks, Computer; D006435:Renal Dialysis / Q000379:methods*