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Machine Learning for Normal Tissue Complication Probability prediction: Predictive power with versatility and easy implementation

dc.contributor.authorHall, E
dc.date.accessioned2023-02-07T14:08:11Z
dc.date.available2023-02-07T14:08:11Z
dc.identifier.citationClinical and Translational Radiation Oncology,en_US
dc.identifier.issn2405-6308
dc.identifier.urihttps://repository.icr.ac.uk/handle/internal/5678
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.relation.ispartofClinical and Translational Radiation Oncology
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/en_US
dc.titleMachine Learning for Normal Tissue Complication Probability prediction: Predictive power with versatility and easy implementationen_US
dc.typeJournal Article
dcterms.dateAccepted2023-02-05
dc.date.updated2023-02-06T12:14:34Z
rioxxterms.versionAMen_US
rioxxterms.typeJournal Article/Reviewen_US
pubs.organisational-group/ICR
pubs.organisational-group/ICR/Primary Group
pubs.organisational-group/ICR/Primary Group/ICR Divisions
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies
pubs.organisational-group/ICR/Primary Group/ICR Divisions/Clinical Studies/Clinical Trials & Statistics Unit
pubs.publication-statusAccepted
icr.researchteamClin Trials & Stats Uniten_US
dc.contributor.icrauthorHall, Emma
icr.provenanceDeposited by Mrs Jessica Perry (impersonating Prof Emma Hall) on 2023-02-06. Deposit type is initial. No. of files: 2. Files: Supplementary Material.pdf; Main Manuscript file-ChangesApplied.pdf


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