dc.contributor.author | Kazmierska, Joanna | |
dc.contributor.author | Hope, Andrew | |
dc.contributor.author | Spezi, Emiliano | |
dc.contributor.author | Beddar, Sam | |
dc.contributor.author | Nailon, William H. | |
dc.contributor.author | Osong, Biche | |
dc.contributor.author | Ankolekar, Anshu | |
dc.contributor.author | Choudhury, Ananya | |
dc.contributor.author | Dekker, Andre | |
dc.contributor.author | Redalen, Kathrine | |
dc.contributor.author | Traverso, Alberto | |
dc.date.accessioned | 2021-02-04T11:18:36Z | |
dc.date.available | 2021-02-04T11:18:36Z | |
dc.date.created | 2020-12-15T13:07:22Z | |
dc.date.issued | 2020 | |
dc.identifier.citation | Radiotherapy and Oncology. 2020, 153 43-54. | en_US |
dc.identifier.issn | 0167-8140 | |
dc.identifier.uri | https://hdl.handle.net/11250/2726131 | |
dc.description.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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | From multisource data to clinical decision aids in radiation oncology: The need for a clinical data science community | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 43-54 | en_US |
dc.source.volume | 153 | en_US |
dc.source.journal | Radiotherapy and Oncology | en_US |
dc.identifier.doi | 10.1016/j.radonc.2020.09.054 | |
dc.identifier.cristin | 1860032 | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |