Kazmierska, Joanna, Hope, Andrew, Spezi, Emiliano, Beddar, Sam, Nailon, William H., Osong, Biche, Ankolekar, Anshu, Choudhury, Ananya, Dekker, Andre, Redalen, Kathrine Røe and Traverso, Alberto
2020.
From multisource data to clinical decision aids in radiation oncology: the need for a clinical data science community.
Radiotherapy and Oncology
153
, pp. 43-54.
10.1016/j.radonc.2020.09.054
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Abstract
Big data are no longer an obstacle; now, by using artificial intelligence (AI), previously undiscovered knowledge can be found in massive data collections. The radiation oncology clinic daily produces a large amount of multisource data and metadata during its routine clinical and research activities. These data involve multiple stakeholders and users. Because of a lack of interoperability, most of these data remain unused, and powerful insights that could improve patient care are lost. Changing the paradigm by introducing powerful AI analytics and a common vision for empowering big data in radiation oncology is imperative. However, this can only be achieved by creating a clinical data science community in radiation oncology. In this work, we present why such a community is needed to translate multisource data into clinical decision aids.
Item Type: | Article |
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Date Type: | Publication |
Status: | Published |
Schools: | Engineering |
Additional Information: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/) |
Publisher: | Elsevier |
ISSN: | 0167-8140 |
Date of First Compliant Deposit: | 29 October 2020 |
Date of Acceptance: | 20 September 2020 |
Last Modified: | 07 Jan 2021 14:04 |
URI: | http://orca.cf.ac.uk/id/eprint/136006 |
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