@article{ad2eab2a39964b9abb5caeb7d2806ed8,
title = "A data science approach for early-stage prediction of Patient's susceptibility to acute side effects of advanced radiotherapy",
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",
author = "M. Aldraimli and D. Soria and D. Grishchuck and S. Ingram and R. Lyon and A. Mistry and J. Oliveira and R. Samuel and L.E.A. Shelley and S. Osman and M.V. Dwek and D. Azria and J. Chang-Claude and S. Gutierrez-Enriquez and {De Santis}, M.C. and B.S. Rosenstein and {De Ruysscher}, D. and E. Sperk and R.P. Symonds and H. Stobart and A. Vega and L. Veldeman and A. Webb and C.J. Talbot and C.M. West and T. Rattay and T.J. Chaussalet and {REQUITE consortium}",
year = "2021",
month = aug,
day = "1",
doi = "10.1016/j.compbiomed.2021.104624",
language = "English",
volume = "135",
journal = "Computers in Biology and Medicine",
issn = "0010-4825",
publisher = "Elsevier Science",
}