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The 'Digital Twin' to enable the vision of precision cardiology
- Source :
- European Heart Journal Supplements, European Heart Journal Supplements, Oxford University Press (OUP), In press, pp.1-11. ⟨10.1093/eurheartj/ehaa159⟩, INRIA a CCSD electronic archive server, Mémoires en Sciences de l'Information et de la Communication, Hal-Diderot, Recolector de Ciencia Abierta, RECOLECTA, UnpayWall, ORCID, Microsoft Academic Graph, PubMed Central, Hyper Article en Ligne, Lirias, Oskar Bordeaux, Sygma, NARCIS, Digital Repository of University of Zaragoza, European Heart Journal, Oxford University Press (OUP): Policy B, 2020, 41 (48), pp.4556-4564. ⟨10.1093/eurheartj/ehaa159⟩, Universidad de Zaragoza, Zaguán: Repositorio Digital de la Universidad de Zaragoza, European Heart Journal, European Heart Journal, 2020, 41 (48), pp.4556-4564. ⟨10.1093/eurheartj/ehaa159⟩, Zaguán. Repositorio Digital de la Universidad de Zaragoza, instname
- Publication Year :
- 2020
- Publisher :
- HAL CCSD, 2020.
-
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. ispartof: EUROPEAN HEART JOURNAL vol:41 issue:48 pages:4556-+ ispartof: location:England status: published
- Subjects :
- Artificial intelligence
Cardiac & Cardiovascular Systems
INFORMATION
diagnosis
030204 cardiovascular system & hematology
0302 clinical medicine
CHANNEL
Medicine
AcademicSubjects/MED00200
RISK
0303 health sciences
Computational model
ARTIFICIAL-INTELLIGENCE
Precision medicine
twins
3. Good health
cardiology
HEART
TRIAL
Cardiology and Cardiovascular Medicine
Life Sciences & Biomedicine
Algorithms
Digital Health and Innovation
vision
Boosting (machine learning)
BIG DATA
Cardiovascular research
MEDLINE
PRESSURE
03 medical and health sciences
models
[SDV.MHEP.CSC]Life Sciences [q-bio]/Human health and pathology/Cardiology and cardiovascular system
computer simulation
Extensive data
State of the Art Review
Humans
030304 developmental biology
Science & Technology
BLOOD-FLOW
business.industry
MEDICINE
Statistical model
PERFORMANCE
CT ANGIOGRAPHY
Data science
Digital twin
Computational modelling
Cardiovascular System & Cardiology
Position paper
precision
business
statistical
Subjects
Details
- Language :
- English
- ISSN :
- 1520765X, 0195668X, and 15229645
- Database :
- OpenAIRE
- Journal :
- European Heart Journal Supplements, European Heart Journal Supplements, Oxford University Press (OUP), In press, pp.1-11. ⟨10.1093/eurheartj/ehaa159⟩, INRIA a CCSD electronic archive server, Mémoires en Sciences de l'Information et de la Communication, Hal-Diderot, Recolector de Ciencia Abierta, RECOLECTA, UnpayWall, ORCID, Microsoft Academic Graph, PubMed Central, Hyper Article en Ligne, Lirias, Oskar Bordeaux, Sygma, NARCIS, Digital Repository of University of Zaragoza, European Heart Journal, Oxford University Press (OUP): Policy B, 2020, 41 (48), pp.4556-4564. ⟨10.1093/eurheartj/ehaa159⟩, Universidad de Zaragoza, Zaguán: Repositorio Digital de la Universidad de Zaragoza, European Heart Journal, European Heart Journal, 2020, 41 (48), pp.4556-4564. ⟨10.1093/eurheartj/ehaa159⟩, Zaguán. Repositorio Digital de la Universidad de Zaragoza, instname
- Accession number :
- edsair.doi.dedup.....3c97ae7c451477bf2bd40d8067a5c65e