A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy

M. Aldraimli*, D. Soria, D. Grishchuck, S. Ingram, R. Lyon, A. Mistry, J. Oliveira, R. Samuel, L.E.A. Shelley, S. Osman, M.V. Dwek, D. Azria, J. Chang-Claude, S. Gutierrez-Enriquez, M.C. De Santis, B.S. Rosenstein, D. De Ruysscher, E. Sperk, R.P. Symonds, H. StobartA. Vega, L. Veldeman, A. Webb, C.J. Talbot, C.M. West*, T. Rattay, T.J. Chaussalet, REQUITE consortium

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Original languageEnglish
Article number104624
Number of pages20
JournalComputers in Biology and Medicine
Volume135
DOIs
Publication statusPublished - 1 Aug 2021

Keywords

  • Classification
  • REQUITE
  • Machine learning
  • Imbalanced learning
  • Radiotherapy
  • SMOTE
  • Meta-learning
  • Desquamation
  • Early toxicities
  • ACUTE SKIN TOXICITY
  • BREAST-CANCER
  • RADIATION
  • MODEL
  • PARAMETERS
  • THERAPY
  • COHORT
  • CURVE
  • NTCP

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