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@ARTICLE{Aldraimli:169807,
      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. Gutiérrez-Enríquez and M.
                      C. De Santis and B. S. Rosenstein and D. De Ruysscher 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},
      collaboration = {R. consortium},
      title        = {{A} data science approach for early-stage prediction of
                      {P}atient's susceptibility to acute side effects of advanced
                      radiotherapy.},
      journal      = {Computers in biology and medicine},
      volume       = {135},
      issn         = {0010-4825},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {DKFZ-2021-01561},
      pages        = {104624},
      year         = {2021},
      abstract     = {The prediction by classification of side effects incidence
                      in a given medical treatment is a common challenge in
                      medical research. Machine Learning (ML) methods are widely
                      used in the areas of risk prediction and classification. The
                      primary objective of such algorithms is to use several
                      features to predict dichotomous responses (e.g., disease
                      positive/negative). Similar to statistical inference
                      modelling, ML modelling is subject to the class imbalance
                      problem and is affected by the majority class, increasing
                      the false-negative rate. In this study, seventy-nine ML
                      models were built and evaluated to classify approximately
                      2000 participants from 26 hospitals in eight different
                      countries into two groups of radiotherapy (RT) side effects
                      incidence based on recorded observations from the
                      international study of RT related toxicity 'REQUITE'. We
                      also examined the effect of sampling techniques and
                      cost-sensitive learning methods on the models when dealing
                      with class imbalance. The combinations of such techniques
                      used had a significant impact on the classification. They
                      resulted in an improvement in incidence status prediction by
                      shifting classifiers' attention to the minority group. The
                      best classification model for RT acute toxicity prediction
                      was identified based on domain experts' success criteria.
                      The Area Under Receiver Operator Characteristic curve of the
                      models tested with an isolated dataset ranged from 0.50 to
                      0.77. The scale of improved results is promising and will
                      guide further development of models to predict RT acute
                      toxicities. One model was optimised and found to be
                      beneficial to identify patients who are at risk of
                      developing acute RT early-stage toxicities as a result of
                      undergoing breast RT ensuring relevant treatment
                      interventions can be appropriately targeted. The design of
                      the approach presented in this paper resulted in producing a
                      preclinical-valid prediction model. The study was developed
                      by a multi-disciplinary collaboration of data scientists,
                      medical physicists, oncologists and surgeons in the UK
                      Radiotherapy Machine Learning Network.},
      keywords     = {Classification (Other) / Desquamation (Other) / Early
                      toxicities (Other) / Imbalanced learning (Other) / Machine
                      learning (Other) / Meta-learning (Other) / REQUITE (Other) /
                      Radiotherapy (Other) / SMOTE (Other)},
      cin          = {C020},
      ddc          = {570},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:34247131},
      doi          = {10.1016/j.compbiomed.2021.104624},
      url          = {https://inrepo02.dkfz.de/record/169807},
}