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; Traverso, Alberto
Journal article, Peer reviewed
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Date
2020Metadata
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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.