Journal article
The ‘Digital Twin’ to enable the vision of precision cardiology
- Abstract:
- Providing therapies tailored to each patient is the vision of precision medicine, enabled by the increasing ability to capture extensive data about individual patients. In this position paper, we argue that the second enabling pillar towards this vision is the increasing power of computers and algorithms to learn, reason, and build the ‘digital twin’ of a patient. Computational models are boosting the capacity to draw diagnosis and prognosis, and future treatments will be tailored not only to current health status and data, but also to an accurate projection of the pathways to restore health by model predictions. The early steps of the digital twin in the area of cardiovascular medicine are reviewed in this article, together with a discussion of the challenges and opportunities ahead. We emphasize the synergies between mechanistic and statistical models in accelerating cardiovascular research and enabling the vision of precision medicine.
- Publication status:
- Published
- Peer review status:
- Peer reviewed
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(Preview, Version of record, pdf, 2.1MB, Terms of use)
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- Publisher copy:
- 10.1093/eurheartj/ehaa159
Authors
- Publisher:
- Oxford University Press
- Journal:
- European Heart Journal More from this journal
- Article number:
- ehaa159
- Publication date:
- 2020-03-04
- Acceptance date:
- 2020-02-24
- DOI:
- EISSN:
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1522-9645
- ISSN:
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0195-668X
- Language:
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English
- Keywords:
- Subjects:
- Pubs id:
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1091177
- Local pid:
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pubs:1091177
- Deposit date:
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2020-03-05
- ARK identifier:
Terms of use
- Copyright holder:
- Corral-Acero et al.
- Copyright date:
- 2020
- Rights statement:
- © The Author(s) 2020. Published by Oxford University Press on behalf of the European Society of Cardiology.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
- Licence:
- CC Attribution (CC BY)
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