Artificial neural networks for adaptive control of profiled haemodialysis in patients with renal insufficiency

Abstract

Background and objective: Currently, haemodialysis treatment is performed using an open-loop control approach, with initial settings of parameters such as ultrafiltration rate and dialyser composition being adapted to the current haemodynamic condition of each patient, although unexpected events may require additional adjustments to be made. Therefore, an artificial neural network-based approach has been presented to automatically control the ultrafiltration rate according to the specific patient conditions during the haemodialysis session, in order to regulate body weight loss, and the elimination of electrolytes and uremic toxins. Methods: This modelling task is performed using a mathematical model of fluid and solute exchange based on first principles, which is used to simulate the process of a haemodialysis session in a specific patient under SIMULINK in order to define the underlying dynamic equations. Alongside this, MATLAB neural network tools are used to adjust the settings of the automatic controller for different body weight loss regulation profiles and variable dialysate sodium conditions during haemodialysis treatment. Results: Computer simulation results show the adequate performance of the body weight loss neuroadaptive control system when submitted to different haemodialysis patterns, uremic toxins and sodium elimination evolution under changing dialysate sodium conditions. Conclusions: The proposed approach proves to be a valuable tool as a test bench for the assessment of alternate haemodialysis profiles aimed to improve the treatment of patients by preventing dialysis-induced haemodynamic complications. The adaptive nature of the model-based control approach here