SmartVent: Use of algorithms applied to physiological signals to personalise care for critically ill patients under mechanical ventilation

Project focused on developing advanced algorithms applied to physiological signals to improve monitoring of critically ill patients under mechanical ventilation. It analyses patient–ventilator interaction through real-time clinical data processing, enabling identification of asynchronies and risk patterns. Its approach contributes to more personalised, data-driven intensive care aimed at improving clinical outcomes in ICU environments.

To characterise patient–ventilator asynchronies and assess their clinical impact using data-driven models. The project aims to develop tools that enable personalised mechanical ventilation, reduce complications and optimise outcomes for critically ill patients. It also seeks to support clinical decision-making through objective information automatically derived from complex physiological signals.

• Implementation of advanced predictive models to personalise clinical management of critically ill patients.
• Optimisation of respiratory interaction to reduce asynchronies.
• Development of algorithm-based analysis and monitoring tools for advanced critical care environments.