Artificial intelligence and digital twins for the personalised prediction of hypertension risk.

Journal: Computers in biology and medicine
Published Date: Jul 7, 2025

Abstract

Hypertension is a significant global health challenge, contributing substantially to morbidity and mortality through its association with various cardiovascular diseases. Traditional approaches to hypertension risk prediction, which rely on broad epidemiological data and common risk factors, often fail to account for individual variability, highlighting the need for advanced data-driven methodologies. This review examines the role of Artificial Intelligence (AI) and Machine Learning (ML) in enhancing the prediction of hypertension risk by incorporating a range of data sources, including clinical, lifestyle, and genetic factors. Despite promising developments, challenges such as data standardisation, the need for high-quality datasets, model explainability, and class imbalance in medical data persist. The integration of wearable technologies, alongside the potential of emerging technologies in healthcare such as digital twins, presents significant opportunities in personalising care through the dynamic modelling of individual health profiles. This review synthesises current methodologies, identifies existing gaps, and highlights the transformative potential of AI-driven, personalised hypertension prevention and management, emphasising the importance of addressing issues of reproducibility and transparency to facilitate clinical adoption.

Authors

  • Akhil Naik
    Data Science Research Centre, Liverpool John Moores University, Liverpool, L3 3AF, UK; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
  • Jakub Nalepa
    Future Processing, Bojkowska 37A, 44-100 Gliwice, Poland; Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland. Electronic address: jakub.nalepa@polsl.pl.
  • Agata M Wijata
    Faculty of Biomedical Engineering, Silesian University of Technology, Roosevelta 40, 41-800 Zabrze, Poland.
  • Joseph Mahon
    Data Science Research Centre, Liverpool John Moores University, Liverpool, L3 3AF, UK; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
  • Dharmesh Mistry
    Data Science Research Centre, Liverpool John Moores University, Liverpool, L3 3AF, UK; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
  • Adam T Knowles
    Data Science Research Centre, Liverpool John Moores University, Liverpool, L3 3AF, UK; Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK.
  • Ellen A Dawson
    Liverpool Centre for Cardiovascular Science at University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, Liverpool, UK; Research Institute for Sport and Exercise Science, Liverpool John Moores University, Liverpool L3 3AF, UK.
  • Gregory Y H Lip
    Liverpool Centre for Cardiovascular Science, University of Liverpool, Liverpool John Moores University and Liverpool Heart & Chest Hospital, L69 3BX Liverpool, UK.
  • Ivan Olier
    1Manchester Metropolitan University, Manchester, UK.
  • Sandra Ortega-Martorell
    School of Computer Science and Mathematics, Liverpool John Moores University, 3 Byrom Street, Liverpool L3 3AF, UK.

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