From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.

Journal: BMJ oncology
Published Date: Nov 2, 2024

Abstract

OBJECTIVES: Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clearance of subsequent treatment is not hindered; however, frequency and timing are not optimal. Model bias and data heterogeneity concerns have hampered the ability of machine learning (ML) to be deployed into clinical practice. This study aims to develop models that could support individualised decisions on the timing of renal and hepatic monitoring while exploring the effect of data shift on model performance.

Authors

  • Matthew Watson
    Department of Computer Science, Durham University, Durham, UK.
  • Pinkie Chambers
    Cancer Division, University College London Hospitals NHS Foundation Trust, London, UK.
  • Luke Steventon
    Cancer Division, University College London Hospitals NHS Foundation Trust, London, UK.
  • James Harmsworth King
    Evergreen Life Ltd, Manchester, UK.
  • Angelo Ercia
    Evergreen Life Ltd, Manchester, UK.
  • Heather Shaw
    Cancer Division, University College London Hospitals NHS Foundation Trust, London, UK.
  • Noura Al Moubayed
    Department of Computer Science, Durham University, Durham, UK.

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