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Improved risk prediction of chemotherapy-induced neutropenia-model development and validation with real-world data.

Mikko S VenäläinenEetu HeerväOuti HirvonenSohrab SaraeiTomi SuomiToni MikkolaMaarit BärlundSirkku JyrkkiöTarja LaitinenLaura L Elo
Published in: Cancer medicine (2021)
Our study demonstrates that real-world NI risk prediction can be improved with machine learning and that every difference in patient or treatment characteristics can have a significant impact on model performance. Here we outline a novel, externally validated approach which may hold potential to facilitate more targeted use of G-CSFs in the future.
Keyphrases
  • chemotherapy induced
  • machine learning
  • big data
  • artificial intelligence
  • case report
  • current status
  • risk assessment
  • deep learning
  • combination therapy