Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records.

Journal: Studies in health technology and informatics
Published Date: Jun 16, 2020

Abstract

Identifying subtypes of Alzheimer's Disease (AD) can lead towards the creation of personalized interventions and potentially improve outcomes. In this study, we use UK primary care electronic health records (EHR) from the CALIBER resource to identify and characterize clinically-meaningful clusters patients using unsupervised learning approaches of MCA and K-means. We discovered and characterized five clusters with different profiles (mental health, non-typical AD, typical AD, CVD and men with cancer). The mental health cluster had faster rate of progression than all the other clusters making it a target for future research and intervention. Our results demonstrate that unsupervised learning approaches can be utilized on EHR to identify subtypes of heterogeneous conditions.

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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