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Title: A Vision for Leveraging the Concept of Digital Twins to Support the Provision of Personalised Cancer Care.
Epworth Authors: Vaughan, Stephen
Other Authors: Wickramasinghe, Nilmini
Jayaraman, Prem
Zelcer, John
Ulapane, Nalika
Forkan, Abdur
Kaul, Rohit
Keywords: Digital Twin
Mathematical Model
Computational Modeling
Medical Services
Numerical Models
Personalised Treatment Planning
Uterine Cancer
Digital Health
Cancer Services Clinical Institute
Issue Date: 11-Mar-2021
Publisher: IEEE
Citation: EEE Internet Computing, doi: 10.1109/MIC.2021.3065381.
Abstract: Exploring the opportunity for applying digital twins in the healthcare context is an emerging research area that has the potential to support more personalised care. A recognised aspect in cancer care is the need for more personalised treatment planning to complement the recent advances in precision medicine. In this article, we present a classification of digital twins into Grey Box, Surrogate and Black Box models using systems and mathematical modelling theory. We then explore one possible approach, namely a Black Box classification for incorporating the use of digital twins in the context of personalised uterine cancer care. This paper presents one of the first attempts to use digital twins in this capacity and represents an amalgamation of three key domains: clinical, digital health and computer science respectively.
DOI: 10.1109/MIC.2021.3065381
ISSN: 1941-0131
Journal Title: IEEE Internet Computing
Type: Journal Article
Affiliated Organisations: School of Health Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia.
Computer Science and Software Engineering, Swinburne University of Technology, Hawthorn, Victoria, Australia.
Faculty of Health, Arts and Design, Swinburne University of Technology, Hawthorn, Victoria, Australia.
Swinburne University of Technology, Hawthorn, Victoria, Australia.
Appears in Collections:Cancer Services
Health Informatics

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