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When acute kidney injury (AKI) develops in a critically ill patient, the patient's mortality rate increases. Therefore, monitoring of the renal function is designated in the intensive care unit (ICU). A RIFLE score depicts the stages of possible kidney injury: Risk, Injury, Failure, Loss and End stage. If a patient reaches the Failure stage, renal replacement therapy (RRT) should be initiated within the next 24 hours to decrease the patient's mortality rate. This work is meant to monitor clearance variables, fluid balance and diuresis at the bedside of patients in the ICU. Since early prediction of AKI could reduce the patients' mortality rate and since AKI is quite common in patients at the ICU, the early prediction of possible AKI is very important. Therefore, a model is created to predict future values of the monitored variables. The prediction error of these variables is up to 30%, with a standard deviation of up to 15%. This means that the predictions can give a general idea of what the value of the variable could be in a few hours, but the predictions are not good enough to really rely on them. An algorithm is created to visualise the RIFLE score for the two approaches (urine output and the combination of glomerular filtration rate and creatinine level). This way it is easier to interpret the patient's kidney status. The monitored data could undergo some unforeseen changes. These changes are traced by the error between the actual data and a model of them. If they differ a lot, a large error is obtained. This probably means that the patient undergoes some unpredicted physiological changes. An alarm is set to warn the caregivers at the ICU only if the error is large enough to exceed a certain threshold. This reduces the amount of alarms in the ICU, which is good for the caregivers who suffer from alarm fatigue. Certain unpredictable physiological changes can be detected for patients at the ICU and the prediction of future values gives an insight in what the possible future course will be of the patient's variables.