Risk prediction of 30-day mortality after stroke using machine learning: a nationwide registry-based cohort study.

Journal: BMC neurology
Published Date: May 27, 2022

Abstract

BACKGROUNDS: We aimed to develop and validate machine learning (ML) models for 30-day stroke mortality for mortality risk stratification and as benchmarking models for quality improvement in stroke care.

Authors

  • Wenjuan Wang
    School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King's College London, London, United Kingdom.
  • Anthony G Rudd
    School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King's College London, London, United Kingdom.
  • Yanzhong Wang
    School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King's College London, London, United Kingdom.
  • Vasa Curcin
    Department of Population Health Sciences King's College London London UK.
  • Charles D Wolfe
    School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King's College London, London, United Kingdom.
  • Niels Peek
    Health e-Research Centre, University of Manchester, Vaughan House, Portsmouth Street, Manchester M13 9GB, UK. Electronic address: niels.peek@manchester.ac.uk.
  • Benjamin Bray
    Medical Director & Head of Epidemiology, EMEA Data Science, IQVIA Europe, Reading, UK.

Keywords

External Resources

Popular Topics
Recent Journals