Project description
AI tools for heart failure risk stratification
Heart failure is a multifaceted clinical syndrome that impacts over 15 million individuals throughout Europe. It is closely linked with diminished quality of life for patients and substantial healthcare expenditures. Consequently, pioneering AI tools can expedite the initiation of preventive and therapeutic approaches. The EU-funded STRATIFYHF project will develop, validate, and implement an AI-driven decision support system through a server and mobile app. This system will serve the purpose of risk stratification, early detection, and evaluation of disease progression, effectively addressing the clinical requirements of both primary and secondary care settings. By amalgamating patient-specific demographic and clinical information through state-of-the-art technologies, the system will employ advanced machine learning and computational modelling techniques to formulate AI-powered instruments for precise risk stratification and accurate prognosis.
Objective
Heart failure (HF) is a pandemic currently affecting up to 15 million people in Europe. It is a complex clinical syndrome presenting with impaired heart function and is associated with poor quality of life for patients and high healthcare costs. There is a high clinical demand for novel artificial intelligence (AI) tools which will facilitate risk stratification, early diagnosis, and disease progression assessment in HF. Such tools are essential to allow prompt initiation of evidence-based prevention and treatment strategies which will improve patient quality of life, reduce morbidity and mortality and the HF burden on healthcare.
STRATIFYHF aims to develop, validate and implement the first AI-based, decision support system (DSS) for risk stratification, early diagnosis, and disease progression assessment in HF to accommodate both primary and secondary care clinical needs. The DSS will integrate patient-specific demographic and clinical data using existing and novel technologies and establish AI-based tools for risk stratification and HF prediction using machine learning. Additionally, a mobile app will be developed to empower patients to better manage their condition, and health care professionals to make informed decision in selection of evidence-based HF prevention and treatment strategies.
Our multidisciplinary consortium, including three small-to-medium enterprises (SMEs) and two stakeholder organisations, will be guided by medical advice and regulatory and health technology experts to deliver the DSS as a medical class 2b device, reaching TRL 8 by the end of the project. STARTIFYHF will change the way in which HF is diagnosed today and thereby improve the quality and length of patients’ lives and lead to efficient and sustainable healthcare systems by reducing the number of HF-related hospital admissions and unnecessary referrals from primary to secondary care in Europe and beyond.
Fields of science (EuroSciVoc)
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CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques.
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Funding Scheme
HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinator
34000 Kragujevac
Serbia
The organization defined itself as SME (small and medium-sized enterprise) at the time the Grant Agreement was signed.