Precision-Optimised Post-Stroke Prognoses.

Journal: Annals of clinical and translational neurology
Published Date: Jun 12, 2025

Abstract

BACKGROUND: Current medicine cannot confidently predict who will recover from post-stroke impairments. Researchers have sought to bridge this gap by treating the post-stroke prognostic problem as a machine learning problem, reporting prediction error metrics across samples of patients whose outcomes are known. This approach effectively shares prediction error equally among the patients, which is contrary to the long-held clinical intuition that some patients' outcomes are more predictable than other patients' outcomes. Here, we test that intuition empirically, by asking whether those 'more predictable' patients can be identified before their outcomes are known.

Authors

  • Thomas M H Hope
    Wellcome Centre for Human Neuroimaging, University College London, UK.
  • Howard Bowman
    The School of Computing, University of Kent, United Kingdom; School of Psychology, University of Birmingham, United Kingdom.
  • Rachel M Bruce
    Department of Imaging Neuroscience, Institute of Neurology, University College London, London, UK.
  • Alex P Leff
    Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK.
  • Cathy J Price
    Wellcome Trust Centre for Neuroimaging, Institute of Neurology, UCL, UK. Electronic address: c.j.price@ucl.ac.uk.

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