The use of clustering to understand disease progression in Rheumatoid Arthritis

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In this paper we examine data representing patients with Rheumatoid Arthritis (RA). This is an important medical conditions that affects a proportion of the adult population and is very disabling. The data contains some demographics as well as follow up for up to 20 years where objective measures of 'joint involvement', e.g. counts of how many tender or swollen joints are present in a given follow up year, are recorded.To date the patterns of disease progression and joint involvement have not been investigated in detail for RA. We propose a clustering approach to extract patterns of joint involvement in disease progression for groups of patients. For this, we investigate how to measure distance for the type of data we analyse which consists of multiple attributes each corresponding to years of follow up measuring a particular objective measure. We settle for an aggregate Dynamic Time Warping measure of distance between patients and use it in combination with K-means clustering to cluster our patient trajectories. Our preliminary results, with some interpretation, show that it is possible to cluster such complex data to extract some meaningful patterns of joint involvement in disease progression.

Original languageEnglish
Title of host publicationIEEE MetroXRAINE 2022
Subtitle of host publicationProceedings of 2022 IEEE International Conference on Metrology for eXtended Reality, Artificial Intelligence and Neural Engineering
Place of PublicationRome
PublisherThe Institute of Electrical and Electronics Engineers (IEEE)
Number of pages5
ISBN (Electronic)978-1-6654-8573-9
Publication statusPublished - 28 Oct 2022

Publication series

Name2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings


  • Clustering
  • rheumatoid arthritis
  • sequence distance metrics
  • unsupervised learning

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