From multisource data to clinical decision aids in radiation oncology: the need for a clinical data science community

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

Preview

PDF - Published Version Available under License Creative Commons Attribution No Derivatives. Download (1MB) | Preview

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
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

Actions (repository staff only)

Edit Item