Heart failure is a complex heterogeneous clinical syndrome with several subclasses and the number one reason for death in the EU. Novel treatment strategies to better assess the needs of individual patients are currently being developed and some computational models have reached a level of maturity that makes them suitable for clinical use. However, patient-specific modelling relies on high quality input data that are difficult to obtain in clinical routine (missing data). Furthermore, some treatment strategies work in animal models of heart failure but fail in patients. 

This project focuses on the handling of missing data and the interpretation of mechanistic differences between patients and animal models to enable personalized modelling for improved diagnosis in cardiovascular medicine. It will combine pre-clinical and clinical data, AI methods and mechanistic modelling into a novel concept – the HeartMed platform. The impact of the modelling tools on the decision-making processes will be investigated at the end of the project.