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Well-being trajectories in breast cancer and their predictors: A machine-learning approach.

Evangelos C KarademasEugenia MylonaKetti MazzoccoRuth Pat-HorenczykBerta SousaAlbino J Oliveira-MaiaJose OliveiraIlan RozinerGeorgios StamatakosFatima CardosoHaridimos KondylakisEleni KolokotroniKonstantina KourouRaquel LemosIsabel ManicaGeorge ManikisChiara MarzoratiJohanna MattsonLuzia TravadoChariklia Tziraki-SegalDimitris FotiadisPaula Poikonen-SakselaPanagiotis Simosnull null
Published in: Psycho-oncology (2023)
There is a strong possibility that resilience does not always reflect a stable response pattern, as there might be some interim fluctuations. The use of machine-learning techniques provides a unique opportunity for the identification of illness trajectories and a shortlist of major bio/behavioral predictors. This will facilitate the development of early interventions to prevent a significant deterioration in patient well-being.
Keyphrases
  • machine learning
  • depressive symptoms
  • artificial intelligence
  • case report
  • physical activity
  • social support
  • deep learning
  • bioinformatics analysis