The use of clustering to understand disease progression in Rheumatoid Arthritis

De La Iglesia, Beatriz ORCID: https://orcid.org/0000-0003-2675-5826, Nawongs, Kathapet, Dainty, Jack R. ORCID: https://orcid.org/0000-0002-0056-1233 and Macgregor, Alexander ORCID: https://orcid.org/0000-0003-2163-2325 (2022) The use of clustering to understand disease progression in Rheumatoid Arthritis. In: IEEE MetroXRAINE 2022. 2022 IEEE International Workshop on Metrology for Extended Reality, Artificial Intelligence and Neural Engineering, MetroXRAINE 2022 - Proceedings . The Institute of Electrical and Electronics Engineers (IEEE), Rome, pp. 444-448. ISBN 978-1-6654-8573-9

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Abstract

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.

Item Type: Book Section
Additional Information: Funding Information: We acknowledge support from Grant Number ES/L011859/1, from The Business and Local Government Data Research Centre, funded by the Economic and Social Research Council to provide economic, scientific and social researchers and business analysts with secure data services.
Uncontrolled Keywords: clustering,rheumatoid arthritis,sequence distance metrics,unsupervised learning,artificial intelligence,computer science applications,media technology,neuroscience (miscellaneous),instrumentation ,/dk/atira/pure/subjectarea/asjc/1700/1702
Faculty \ School: Faculty of Science > School of Computing Sciences
Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups: Faculty of Medicine and Health Sciences > Research Centres > Norwich Institute for Healthy Aging
Faculty of Medicine and Health Sciences > Research Centres > Business and Local Government Data Research Centre
Faculty of Science > Research Groups > Norwich Epidemiology Centre
Faculty of Medicine and Health Sciences > Research Groups > Norwich Epidemiology Centre
Faculty of Science > Research Groups > Data Science and Statistics
Faculty of Medicine and Health Sciences > Research Groups > Public Health and Health Services Research
Faculty of Medicine and Health Sciences > Research Groups > Nutrition and Preventive Medicine
Faculty of Medicine and Health Sciences > Research Groups > Musculoskeletal Medicine
Faculty of Medicine and Health Sciences > Research Groups > Epidemiology and Public Health
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Depositing User: LivePure Connector
Date Deposited: 01 Nov 2022 09:30
Last Modified: 22 May 2023 08:30
URI: https://ueaeprints.uea.ac.uk/id/eprint/89453
DOI: 10.1109/MetroXRAINE54828.2022.9967609

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