Model-based physiological data stream evaluation for dialysis therapy

Conventional dialysis therapy ofpatients with end-stage renal disease is time-consuming and its discontinuous nature induces physiological stress to the patient. Therefore new emerging approaches facilitate prolonged treatment by using portable devices in home-settings. Such unsupervised treatment calls for continuous automated monitoring of various physiological parameters. This paper highlights the challenges of autonomous personalized renal treatment and presents a leap towards contextaware monitoring in data streams by a physiological modelbased approach. The algorithmic principle is employed to propagate urea concentration, which is measured to assess dialysis efficacy. Simulation results validate the algorithm for the application in a mobile dialysis therapy currently under development. The presented method enables the estimation of medically significant, but technically inaccessible physiological parameters from data stream propagation.