Cardiovascular models for personalised medicine: Where now and where next?
Cardiovascular models for personalised medicine: Where now and where next?
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DOI：
10.1016/j.medengphy.2019.08.007
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发表时间：
2019-10-01
影响因子：
2.2
通讯作者：
van de Vosse, Frans N.
中科院分区：
文献类型：
--
作者：
Hose, D. Rodney;Lawford, Patricia V.;van de Vosse, Frans N.
本立场文件的目的是简要概述心血管建模的现状、所需的过程以及需要解决的一些挑战，以在个人健康管理和临床实践中得到更广泛的应用。在大多数工程领域，数字孪生的概念通过广泛、持续的监测以及强大的数据同化和模拟技术而受到关注：Gartner Group 将其列为 2018 年十大数字趋势之一。心血管建模界正在开始开发一种更加系统的方法，将物理、数学、控制理论、人工智能、机器学习、计算机科学和先进的工程方法相结合，并与临床界更密切地合作，以更好地理解和利用生理测量，并共同开发更好的技术。基于模型的理解告知测量协议。讨论了生理建模、模型个性化、模型结果不确定性以及模型在临床决策支持中的作用的发展，并讨论了“下一步”步骤和挑战。 (C) 2019 年 IPEM。由爱思唯尔有限公司出版。保留所有权利。
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. (C) 2019 IPEM. Published by Elsevier Ltd. All rights reserved.