A non-invasive and simple method of parameter estimation has been developed for the model-based decision support of the artificial ventilation in intensive care units. The parameter concerned was the respiratory shunt. Originally, the shunt had to be estimated using a numerical algorithm, which was slow and unreliable. The estimation process also required the knowledge of other parameters, whose values could only be obtained using invasive monitoring equipment. In this paper, the respiratory index is used for the shunt estimation. A linear regression model and a non-linear adaptive neuro-fuzzy inference system (ANFIS) model were used to describe the relationship between the respiratory index and the shunt. The shunts estimated using these models were then used to calculate the fractional inspired oxygen needed to attain the target arterial oxygen level of the model patient. The advisor also utilises population median values of the cardiac index and oxygen consumption index. This alleviates the need for invasive monitoring. In a simulation study, the mean squared error of the control using the ANFIS model was 0.75 kPa2 compared to 2.06 kPa2 using the linear regression model. Therefore, the performance of the FiO2 advisor was better when the shunt was estimated using the non-linear ANFIS model.