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Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology
  1. Stephanie J W Shoop-Worrall1,2,
  2. Katherine Cresswell3,4,
  3. Imogen Bolger5,
  4. Beth Dillon5,
  5. Kimme L Hyrich2,3,
  6. Nophar Geifman1,6
  7. Members of the CLUSTER consortium
    1. 1 Centre for Health Informatics, The University of Manchester, Manchester, UK
    2. 2 Centre for Epidemiology Versus Arthritis, The University of Manchester, Manchester, UK
    3. 3 NIHR Manchester BRC, Manchester Academic Health Science Centre, Manchester University NHS Foundation Trust, Manchester, UK
    4. 4 Vocal, Manchester University NHS Foundation Trust, Manchester, UK
    5. 5 Your Rheum, Young Person’s Research Advisory Group, Manchester, UK
    6. 6 Faculty of Health and Medical Sciences, University of Surrey, Guildford, Surrey, UK
    1. Correspondence to Dr Stephanie J W Shoop-Worrall, Centre for Health Informatics, The University of Manchester, Manchester, UK; Stephanie.shoop-worrall{at}manchester.ac.uk

    Abstract

    Novel machine learning methods open the door to advances in rheumatology through application to complex, high-dimensional data, otherwise difficult to analyse. Results from such efforts could provide better classification of disease, decision support for therapy selection, and automated interpretation of clinical images. Nevertheless, such data-driven approaches could potentially model noise, or miss true clinical phenomena. One proposed solution to ensure clinically meaningful machine learning models is to involve primary stakeholders in their development and interpretation. Including patient and health care professionals’ input and priorities, in combination with statistical fit measures, allows for any resulting models to be well fit, meaningful, and fit for practice in the wider rheumatological community. Here we describe outputs from workshops that involved healthcare professionals, and young people from the Your Rheum Young Person’s Advisory Group, in the development of complex machine learning models. These were developed to better describe trajectory of early juvenile idiopathic arthritis disease, as part of the CLUSTER consortium. We further provide key instructions for reproducibility of this process.Involving people living with, and managing, a disease investigated using machine learning techniques, is feasible, impactful and empowering for all those involved.

    • arthritis
    • juvenile
    • epidemiology
    • outcome assessment
    • outcome and process assessment
    • health care
    • patient reported outcome measures
    https://creativecommons.org/licenses/by/4.0/

    This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/.

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    Footnotes

    • Handling editor Josef S Smolen

    • Twitter @sshoopworrall, @NopharGeifman

    • Collaborators Members of the CLUSTER consortium: Prof Lucy R. Wedderburn; Dr Melissa Kartawinata; Ms Elizabeth Ralph; Mr Fatjon Dekaj; Ms Beth Jebson; Ms Zoe Wanstall; Ms Aline Kimonyo; Ms Fatema Merali; Ms Emma Sumner; Ms Emily Robinson; Prof Andrew Dick; Prof Michael W. Beresford; Dr Emil Carlsson; Dr Joanna Fairlie; Dr Jenna F. Gritzfeld; Prof Athimalaipet Ramanan; Ms Teresa Duerr; Prof Michael Barnes; Ms Sandra Ng; Prof Wendy Thomson; Prof Kimme Hyrich; Dr Nophar Geifman; Prof Soumya Raychaudhuri; Prof Andrew Morris; Dr Annie Yarwood; Dr Samantha Smith; Dr Damian Tarasek; Dr Stephanie Shoop-Worrall; Ms Saskia Lawson-Tovey; Dr Paul Martin; Prof Stephen Eyre; Dr Chris Wallace; Dr Wei-Yu Lin; Dr Toby Kent; Dr Thierry Sornasse;, Dr Jessica, Neisen; Dr Sally-Anne Dews; Dr Gil Reynolds Diogo; Dr John Ioannou; Dr Hussein Al-Mossawi and Dr Helen Neale.

    • Contributors SJWS-W: Contributed to the design, data acquisition, analysis, interpretation of data, drafting the work, approved the final version and agrees to be accountable for all aspects of the work. KC: Contributed to the design, data acquisition, analysis, interpretation of data, revising the work for important intellectual content, approved the final version and agrees to be accountable for all aspects of the work. IB, BD, KLH and NG: Contributed to the analysis, interpretation of data, drafting the work, approved the final version and agrees to be accountable for all aspects of the work.

    • Funding CLUSTER was supported by grants from the Medical Research Council (MR/R013926/1) and Versus Arthritis (Grant: 22084), Great Ormond Street Hospital Children’s Charity (VS0518) and Olivia’s Vision. This work was supported by the NIHR GOSH BRC, the NIHR Manchester Biomedical Research Centre, the NIHR GOSH Biomedical Research Centre, the British Society for Rheumatology and the UK’s Experimental Arthritis Treatment Centre for Children, supported by Versus Arthritis [grant: 20621]. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. KLH was additionally supported by the Centre for Epidemiology Versus Arthritis (grant: 21755) at the University of Manchester, UK. This study acknowledges the use of the following UK JIA cohort collection: Childhood Arthritis Prospective Study (funded by Versus Arthritis UK, grant: 20542).

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were involved in the design, or conduct, or reporting, or dissemination plans of this research. Refer to the Methods section for further details.

    • Provenance and peer review Not commissioned; externally peer reviewed.

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