A systematic review of machine learning models for predicting outcomes of stroke with structured data.

Journal: PloS one
Published Date: Jun 12, 2020

Abstract

BACKGROUND AND PURPOSE: Machine learning (ML) has attracted much attention with the hope that it could make use of large, routinely collected datasets and deliver accurate personalised prognosis. The aim of this systematic review is to identify and critically appraise the reporting and developing of ML models for predicting outcomes after stroke.

Authors

  • Wenjuan Wang
    School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King's College London, London, United Kingdom.
  • Martin Kiik
    School of Medical Education, 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.
  • Vasa Curcin
    Department of Population Health Sciences King's College London London UK.
  • Iain J Marshall
    Department of Primary Care and Public Health Sciences, King's College London, UK iain.marshall@kcl.ac.uk.
  • 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.
  • Abdel Douiri
    School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King's College London, London, United Kingdom.
  • Charles D Wolfe
    School of Population Health & Environmental Sciences, Faculty of Life Science and Medicine, King's College London, London, United Kingdom.
  • Benjamin Bray
    Medical Director & Head of Epidemiology, EMEA Data Science, IQVIA Europe, Reading, UK.

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