Hose, D.R., Lawford, P.V. orcid.org/0000-0001-7128-3843, Huberts, W. et al. (3 more authors) (2019) Cardiovascular models for personalised medicine: Where now and where next? Medical Engineering and Physics, 72. pp. 38-48. ISSN 1350-4533
Abstract
The aim of this position paper is to provide a brief overview of the current status of cardiovascular modelling and of the processes required and some of the challenges to be addressed to see wider exploitation in both personal health management and clinical practice. In most branches of engineering the concept of the digital twin, informed by extensive and continuous monitoring and coupled with robust data assimilation and simulation techniques, is gaining traction: the Gartner Group listed it as one of the top ten digital trends in 2018. The cardiovascular modelling community is starting to develop a much more systematic approach to the combination of physics, mathematics, control theory, artificial intelligence, machine learning, computer science and advanced engineering methodology, as well as working more closely with the clinical community to better understand and exploit physiological measurements, and indeed to develop jointly better measurement protocols informed by model-based understanding. Developments in physiological modelling, model personalisation, model outcome uncertainty, and the role of models in clinical decision support are addressed and ‘where-next’ steps and challenges discussed.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2019 IPEM. Published by Elsevier Ltd. This is an author produced version of a paper subsequently published in Medical Engineering and Physics. Uploaded in accordance with the publisher's self-archiving policy. Article available under the terms of the CC-BY-NC-ND licence (https://creativecommons.org/licenses/by-nc-nd/4.0/). |
Keywords: | Cardiovascular modelling; Model personalisation; Model; Uncertainity; Physiological modelling; Clinical descision support |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Sheffield Teaching Hospitals |
Funding Information: | Funder Grant number EUROPEAN COMMISSION - HORIZON 2020 689617 |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 29 Jan 2020 14:24 |
Last Modified: | 24 Sep 2020 00:45 |
Status: | Published |
Publisher: | Elsevier |
Refereed: | Yes |
Identification Number: | 10.1016/j.medengphy.2019.08.007 |
Related URLs: | |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:152882 |