The prediction of respiratory dynamics is recommended for improving the care of patients with respiratory diseases. Indeed, several authors have integrated formal or computational techniques and have widely used the single compartment model for this purpose. The approach of inverse modeling has been used to establish correlations between the inspiratory and expiratory phases, aiming to derive equations that relate both phases in patients and predict ventilatory parameters during the expiratory phase. To achieve this, the use of image processing for obtaining data from ventilated patients was validated. Subsequently, the ventilatory parameters of the simple compartment model were analyzed, leading to relationships for elastance and two newly defined parameters, referred to as L and H, which contain sufficient information for making predictions regarding the expiratory phase based on inspiratory phase data. It was concluded that while adequate predictions were achieved, there is still room for improvement due to the limitations inherent to the simple compartment model, particularly when studying patients under partial sedation where spontaneous respirations and asynchrony are evident.