The demand for quality and affordable medical services is rapidly increasing worldwide, an increase that is particularly relevant to older adults, due to the growth of this segment in the general population [1]. Also, the risks for cardiovascular diseases (CVDs) and their prevalence increase dramatically and create a heavy economic burden [2]. Among cardiovascular diseases, hypertension is one of the strongest risk factors for all different CVDs.

The RENEW European Project (Reshaping data-driven smart healthcare to optimize resources and personalize care for hypertensive patients through AI and digital twin models) is targeting personalized data-driven smart healthcare to benefit hypertensive older adults, their caregivers, and their medical practitioners. By exploiting the consortium's previous results and expertise to design, implement, and demonstrate innovative solutions that can address current and future challenges, a health monitoring platform will be developed.

We will use reliable data processing and novel machine learning algorithms to learn and build personalised profiles and the Digital Twin of hypertensive older adults. In healthcare, a digital twin refers to a computer simulation that allows to generate biologically realistic data of a target patient [3]. DTs are gaining momentum in healthcare and have the potential to transform diagnostics, treatment, and monitoring of patients and their health, and is predicted that over 66% of companies will deploy at least one DT in production by 2022 [4]. In our project, we will use DT applications as a way to simulate biological processes and educate medical personnel and students. DT can provide a safe and realistic environment for healthcare professionals to enhance their skills, learn and practice complex procedures [5].

References:
[1] World Health Organization: WHO (2024). Ageing and health. https://www.who.int/news-room/fact-sheets/detail/ageing-and-health. Accessed 31st of October 2024.
[2] Roth, G.A. et. al; GBD-NHLBI-JACC Global Burden of Cardiovascular Diseases Writing Group. Global Burden of Cardiovascular Diseases and Risk Factors, 1990-2019: Update from the GBD 2019 Study. J Am Coll Cardiol. 2020 Dec 22;76(25):2982-3021. doi: 10.1016/j.jacc.2020.11.010.
[3] Shao G., Helu M.; Framework for a Digital Twin in Manufacturing: Scope and Requirements. Manuf Lett. 2020;24:10.1016/j.mfglet.2020.04.004. doi: 10.1016/j.mfglet.2020.04.004. PMID: 32832379; PMCID: PMC7431924.
[4] Zhang, K. et. al; Concepts and applications of digital twins in healthcare and medicine; Patterns, Volume 5, Issue 8, 2024, 101028, ISSN 2666-3899, https://doi.org/10.1016/j.patter.2024.101028.
[5] Emmert-Streib F, Yli-Harja O. What Is a Digital Twin? Experimental Design for a Data-Centric Machine Learning Perspective in Health. International Journal of Molecular Sciences. 2022; 23(21):13149. https://doi.org/10.3390/ijms232113149.

Keywords: Hypertension, seniors, digital twin, health platform, international project.