Communicating exploratory unsupervised machine learning analysis in age clustering for paediatric disease.

Journal: BMJ health & care informatics
Published Date: Jul 29, 2024

Abstract

BACKGROUND: Despite the increasing availability of electronic healthcare record (EHR) data and wide availability of plug-and-play machine learning (ML) Application Programming Interfaces, the adoption of data-driven decision-making within routine hospital workflows thus far, has remained limited. Through the lens of deriving clusters of diagnoses by age, this study investigated the type of ML analysis that can be performed using EHR data and how results could be communicated to lay stakeholders.

Authors

  • Joshua William Spear
    DRIVE, Great Ormond Street Hospital for Children, London, UK.
  • Eleni Pissaridou
    DRIVE, Great Ormond Street Hospital for Children, London, UK.
  • Stuart Bowyer
    DRIVE, Great Ormond Street Hospital for Children, London, UK.
  • William A Bryant
    DRIVE, Great Ormond Street Hospital for Children, London, UK.
  • Daniel Key
    DRIVE, Great Ormond Street Hospital for Children, London, UK.
  • John Booth
    Great Ormond Street Hospital, Great Ormond Street Hospital Institute of Child Health and NIHR GOSH BRC, London, UK.
  • Anastasia Spiridou
    DRIVE, Great Ormond Street Hospital for Children, London, UK.
  • Spiros Denaxas
    UCL Institute of Health Informatics and Farr Institute of Health Informatics Research, London, United Kingdom.
  • Rebecca Pope
    Institute of Child Health, University College London, London, UK.
  • Andrew M Taylor
    Great Ormond Street Hospital for Children, Zayed Centre for Research, 20 Guildford St., Room 3.7, London, WC1N 1DZ, UK. a.taylor76@ucl.ac.uk.
  • Harry Hemingway
    Institute of Health Informatics, University College London, London, UK.
  • Neil J Sebire
    Health Data Research UK, London, UK.

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