The vascular access that allows to perform the extra-corporeal circulation, is usually a vein of the arm that has been enlarged by a surgical creation of a fistula. The prevention of complications such as stenosis or thrombosis of the vascular access is a key issue in hemodialysis treatment. Many dialysis machines measure ionic dialysance by conductivity measures on the dialysate fluid. Ionic dialysance is an indicator of small molecules transfers through the dialysis membrane. Previous works have shown that the follow-up of the dialysance and the pressures along the extra corporeal circuit can help to detect at an early stage a potential complication on the vascular access. The difficulty of automating the follow-up is the large variability of the measures and the need to detect tendencies. Dynamic bayesian networks (DBN) allow to formalize expert knowledge as a graphical stochastic model adapted to reasoning under uncertainty. In a DBN the state of the patient and the measurements are represented by interconnected temporal random variables. The relations between those variables are described using probability distributions. The proposed approach is based on a supervised learning of a DBN for classifying the dialysis sessions according to a risk score describing the medical situation (0: no risk,1: mild risk, 2: severe risk). The training of the system was performed using a dataset labelled by a medical expert. The evaluation of the results was done performing a double-blind analysis of real data. The result was an 85% agreement rate between the human expert and the automated analysis. The purpose of the system is to assist the human expert by reporting abnormalities. The results show that a score 2 reported by the human is rarely missed by the automated analysis (only 1 case) whereas the opposite is more frequent (8 cases). The final decision to investigate further a case is taken by the human expert.