ABSTRACT
Heart failure (HF) is a global pandemic currently affecting up to 15 million people in Europe. It is a complex clinical syndrome associated with impaired heart function, poor quality of life for patients and high healthcare costs. STRATIFYHF project is addressed this global challenge with an AI-based Decision Support System for HF.
The STRATIFYHF aims to develop, validate and implement the first artificial intelligence (AI)-based, Decision Support System (DSS) integrated with multiscale computational modeling with big data for assessing and predicting the risk of HF, its early diagnosis and progression. STRATIFYHF project integrates 1) patient-specific data i.e. demographic, clinical, genetic, lifestyle and socio-economic, 2) an AI-based digital patient library and algorithms for risk stratification, early diagnosis, and disease progression, and 3) a highly innovative multifunctional AI-based and computational modelling DSS, big data and mobile app for informing a patient-centred, personalised, prevention and treatment strategies.
In the retrospective phase, Centres managed to collect demographic and clinical data for 5,624 patients with confirmed diagnosis of heart failure, and 4,465 patients with suspected heart failure. In additional, access to Clinical Practice Research Data link allowed access to 11 million patient records who are at risk of developing heart failure, and 680,000 patients with confirmed heart failure. These data have been used to develop risk stratification, diagnostic and prognostic models. We have also analysed and disseminated findings on multimorbidity in heart failure using retrospective data.
Central to the system’s operation is the Workflow Manager Module (WMM), which acts as an orchestrator, employing a Docker Engine to deploy and manage high-computation tasks like the 3D Computer Modeling (PAK) tool for biomechanical heart simulations. Analytical power is provided by the Risk Stratification and Early Diagnosis modules, which apply machine learning algorithms to identify high-risk patients and detect heart failure markers that might be missed during routine exams. Finally, the Visual Analytics Module (VAM) and the User Access Management Module (UAMM) ensure that these complex outputs are translated into intuitive, secure, and interactive dashboards, allowing clinicians to visualize disease progression and voice biomarker trends directly within their workflow STRATIFYHF will change the ways in which HF is managed today, thereby improving the quality and length of patients’ lives. Solution in STRATIFYHF will lead towards an efficient and sustainable healthcare systems by reducing the number of HF-related hospital admissions and deaths, and unnecessary referrals from primary to secondary care.
Biography
Nenad Filipović is a Serbian professor and prominent researcher in mechanical engineering, bioengineering, and computational science, based at the University of Kragujevac in Serbia. Born on 23 February 1970 in Kragujevac, he completed his undergraduate studies and PhD in mechanical engineering at the Faculty of Engineering in the same city, beginning his academic career there in 1994 and becoming a full professor in 2010. Over the years, he has also undertaken international research visits at institutions such as the University of Vienna and Harvard University. His work focuses on applied mechanics, finite element methods, and bioengineering simulations, and he has authored more than 200 scientific papers along with several monographs and research projects, including participation in major European programs like Horizon 2020. In addition to his academic contributions, he has held significant leadership roles, serving as the Rector of the University of Kragujevac and later as Director of the Science Fund of Serbia. Recognized for his scientific achievements, he has received multiple awards, including the Kapetan Miša Anastasijević award, and is regarded as an influential figure in advancing engineering and interdisciplinary research in Serbia.
