Intensive Care Unit (ICU) is a challenging environment, requiring continuous monitoring and treatment adaptations, raising the need for tools and platforms to support medical decisions. In this context, the focus of this work is in supporting clinicians in managing assisted ventilation intervention (AVI). In AVI the need for patient-ventilator coupling exists. Attention may be required in cases when patient’s effort doesn’t trigger the ventilator at all, and the assisted ventilation event is lost, i.e. when an ineffective effort (IE) event takes place. A high exposure to IEs has been related to adverse clinical outcomes. The purpose of this work is to create new features that complement the already existing IE index in terms of estimating the adverse effects of ventilation exposure. A series of tools varying from raw data handling to the creation of predictive models are created and implemented in a custom platform, utilizing open-source software.