Identifying subtypes of type 2 diabetes mellitus with machine learning: development, internal validation, prognostic validation and medication burden in linked electronic health records in 420 448 individuals.

Journal: BMJ open diabetes research & care
Published Date: Jun 4, 2024

Abstract

INTRODUCTION: None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered to an established framework. We aimed to develop and validate machine learning (ML)-informed subtypes for type 2 diabetes mellitus (T2D) using nationally representative data.

Authors

  • Mehrdad A Mizani
    University College London, London, UK.
  • Ashkan Dashtban
    University College London, London, UK.
  • Laura Pasea
    University College London, London, UK.
  • Qingjia Zeng
    University College London, London, UK.
  • Kamlesh Khunti
    Leicester Diabetes Centre, University Hospitals of Leicester, Leicester General Hospital, Gwendolen Road, Leicester LE5 4PW, UK; NIHR Leicester Biomedical Research Centre, Leicester General Hospital, Gwendolen Road, Leicester, LE5 4PW, UK.
  • Jonathan Valabhji
    NHS England and NHS Improvement London, London, UK.
  • Jil Billy Mamza
    AstraZeneca Cambridge Biomedical Campus, Cambridge, UK.
  • He Gao
    Clinical Sleep Medicine Center, The General Hospital of the Air Force, Beijing, 100142, China. Electronic address: bjgaohe@sohu.com.
  • Tamsin Morris
    AstraZeneca, Cambridge, UK.
  • Amitava Banerjee
    School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798 amitavabanerjee.iitkgp@gmail.com.

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