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.
| Original language | English |
|---|---|
| Pages (from-to) | 38-48 |
| Number of pages | 11 |
| Journal | Medical Engineering & Physics |
| Volume | 72 |
| DOIs | |
| Publication status | Published - 1 Oct 2019 |
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Keywords
- Cardiovascular modelling
- Clinical descision support
- Model
- Model personalisation
- Physiological modelling
- Uncertainity
Cite this
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Cardiovascular models for personalised medicine : where now and where next? / Hose, D. Rodney (Corresponding author); Lawford, Patricia V.; Huberts, Wouter; Hellevik, Leif Rune; Omholt, Stig W.; van de Vosse, Frans N.
In: Medical Engineering & Physics, Vol. 72, 01.10.2019, p. 38-48.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Cardiovascular models for personalised medicine
T2 - where now and where next?
AU - Hose, D. Rodney
AU - Lawford, Patricia V.
AU - Huberts, Wouter
AU - Hellevik, Leif Rune
AU - Omholt, Stig W.
AU - van de Vosse, Frans N.
PY - 2019/10/1
Y1 - 2019/10/1
N2 - 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.
AB - 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.
KW - Cardiovascular modelling
KW - Clinical descision support
KW - Model
KW - Model personalisation
KW - Physiological modelling
KW - Uncertainity
UR - http://www.scopus.com/inward/record.url?scp=85072084973&partnerID=8YFLogxK
U2 - 10.1016/j.medengphy.2019.08.007
DO - 10.1016/j.medengphy.2019.08.007
M3 - Article
C2 - 31554575
AN - SCOPUS:85072084973
VL - 72
SP - 38
EP - 48
JO - Medical Engineering & Physics
JF - Medical Engineering & Physics
SN - 1350-4533
ER -