Machine Learning Approaches to Determine Feature Importance for Predicting Infant Autopsy Outcome.

Journal: Pediatric and developmental pathology : the official journal of the Society for Pediatric Pathology and the Paediatric Pathology Society
Published Date: Jan 1, 2021

Abstract

INTRODUCTION: Sudden unexpected death in infancy (SUDI) represents the commonest presentation of postneonatal death. We explored whether machine learning could be used to derive data driven insights for prediction of infant autopsy outcome.

Authors

  • John Booth
    Great Ormond Street Hospital, Great Ormond Street Hospital Institute of Child Health and NIHR GOSH BRC, London, UK.
  • Ben Margetts
    Great Ormond Street Hospital, Great Ormond Street Hospital Institute of Child Health and NIHR GOSH BRC, London, UK.
  • Will Bryant
    Great Ormond Street Hospital, Great Ormond Street Hospital Institute of Child Health and NIHR GOSH BRC, London, UK.
  • Richard Issitt
    Great Ormond Street Hospital, Great Ormond Street Hospital Institute of Child Health and NIHR GOSH BRC, London, UK.
  • Ciaran Hutchinson
    Great Ormond Street Hospital, Great Ormond Street Hospital Institute of Child Health and NIHR GOSH BRC, London, UK.
  • Nigel Martin
    Department of Computer Science and Information Systems, Birkbeck University of London, London, UK.
  • Neil J Sebire
    Health Data Research UK, London, UK.

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