Identifying and evaluating clinical subtypes of Alzheimer's disease in care electronic health records using unsupervised machine learning.

Journal: BMC medical informatics and decision making
Published Date: Dec 8, 2021

Abstract

BACKGROUND: Alzheimer's disease (AD) is a highly heterogeneous disease with diverse trajectories and outcomes observed in clinical populations. Understanding this heterogeneity can enable better treatment, prognosis and disease management. Studies to date have mainly used imaging or cognition data and have been limited in terms of data breadth and sample size. Here we examine the clinical heterogeneity of Alzheimer's disease patients using electronic health records (EHR) to identify and characterise disease subgroups using multiple clustering methods, identifying clusters which are clinically actionable.

Authors

  • Nonie Alexander
    Institute of Health Informatics, University College London, London, UK.
  • Daniel C Alexander
    Centre for Medical Image Computing and Dept of Computer Science, University College London, Gower Street, London WC1E 6BT, UK.
  • Frederik Barkhof
    MS Center Amsterdam, Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, the Netherlands.
  • Spiros Denaxas
    UCL Institute of Health Informatics and Farr Institute of Health Informatics Research, London, United Kingdom.

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