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Challenges and progress in the application of physiological models for clinical decision support in cardiovascular medicine
The IUPS Physiome and the European Commission’s Virtual Physiological Human (VPH), funded under the Framework 7 programme, are just two of the major initiatives that have supported the development of physiological models for clinical decision support. There is significantprogress in the formalisatio...
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Published in: | IOP conference series. Materials Science and Engineering 2022-09, Vol.1254 (1), p.12005 |
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description | The IUPS Physiome and the European Commission’s Virtual Physiological Human (VPH), funded under the Framework 7 programme, are just two of the major initiatives that have supported the development of physiological models for clinical decision support. There is significantprogress in the formalisation of the concept of the Digital Twin for human physiology and in processes for model personalisation, verification and validation. The VPH Institute and the Avicenna Alliance continue to promote best practice in this area. One of the most important challenges is to achieve the right level of complexity in the model. The most comprehensive models seek to capture all that is known about the physiological system, including detailed anatomy, organ interactions, control functions, and physical, chemical and biological processes. These models can be of enormous value in understanding human physiology, but can also be terribly difficult to personalise to support the clinical management of an individual. There is inevitable conflict between model complexity and the pragmatic limitation of data collection in the clinical pathway. In this presentation we make the case for a 3-axis digital twin, including recognition of the individual physiological envelope, and introduce three applications in cardiovascular medicine. |
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subjects | Best practice Biological activity Complexity Decision support systems Digital twins Physiology Three axis |
title | Challenges and progress in the application of physiological models for clinical decision support in cardiovascular medicine |
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