Artificial Intelligence and Digital Twins for the Personalised Prediction of Hypertension Risk

Naik, A, Nalepa, J, Wijata, AM, Mahon, J, Mistry, D, Knowles, AT, Dawson, EA, Lip, GYH, Olier-Caparroso, I and Ortega-Martorell, S ORCID: 0000-0001-9927-3209 (2025) Artificial Intelligence and Digital Twins for the Personalised Prediction of Hypertension Risk. Computers in Biology and Medicine, 196. pp. 1-12. ISSN 0010-4825

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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.

Item Type:	Article
Uncontrolled Keywords:	08 Information and Computing Sciences; 09 Engineering; 11 Medical and Health Sciences; Biomedical Engineering; 3102 Bioinformatics and computational biology; 4203 Health services and systems; 4601 Applied computing
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine R Medicine > RC Internal medicine > RC1200 Sports Medicine
Divisions:	Computer Science and Mathematics Engineering Nursing and Advanced Practice Sport and Exercise Sciences
Publisher:	Elsevier
Date of acceptance:	2 July 2025
Date of first compliant Open Access:	15 July 2025
Date Deposited:	02 Jul 2025 14:58
Last Modified:	15 Jul 2025 13:00
DOI or ID number:	10.1016/j.compbiomed.2025.110718
URI:	https://researchonline.ljmu.ac.uk/id/eprint/26698

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