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Your search keyword '"Worrall, Stephanie"' showing total 4 results
Start Over Author "Worrall, Stephanie" Publisher bmj
4 results on '"Worrall, Stephanie"'

1. EULAR recommendations for the management of psoriatic arthritis with pharmacological therapies: 2023 update

Author

Gossec, Laure, primary, Kerschbaumer, Andreas, additional, Ferreira, Ricardo J O, additional, Aletaha, Daniel, additional, Baraliakos, Xenofon, additional, Bertheussen, Heidi, additional, Boehncke, Wolf-Henning, additional, Esbensen, Bente Appel, additional, McInnes, Iain B, additional, McGonagle, Dennis, additional, Winthrop, Kevin L, additional, Balanescu, Andra, additional, Balint, Peter V, additional, Burmester, Gerd R, additional, Cañete, Juan D, additional, Claudepierre, Pascal, additional, Eder, Lihi, additional, Hetland, Merete Lund, additional, Iagnocco, Annamaria, additional, Kristensen, Lars Erik, additional, Lories, Rik, additional, Queiro, Rubén, additional, Mauro, Daniele, additional, Marzo-Ortega, Helena, additional, Mease, Philip J, additional, Nash, Peter, additional, Wagenaar, Wendy, additional, Savage, Laura, additional, Schett, Georg, additional, Shoop-Worrall, Stephanie J W, additional, Tanaka, Yoshiya, additional, Van den Bosch, Filip E, additional, van der Helm-van Mil, Annette, additional, Zabotti, Alen, additional, van der Heijde, Désirée, additional, and Smolen, Josef S, additional

Published
2024
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3. How common is clinically inactive disease in a prospective cohort of patients with juvenile idiopathic arthritis? The importance of definition

Author

Shoop-Worrall, Stephanie J W, primary, Verstappen, Suzanne M M, additional, Baildam, Eileen, additional, Chieng, Alice, additional, Davidson, Joyce, additional, Foster, Helen, additional, Ioannou, Yiannis, additional, McErlane, Flora, additional, Wedderburn, Lucy R, additional, Thomson, Wendy, additional, and Hyrich, Kimme L, additional

Published
2017
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4. Nothing about us without us: involving patient collaborators for machine learning applications in rheumatology

Author

Stephanie J W Shoop-Worrall, Beth Dillon, Katherine Cresswell, Nophar Geifman, Kimme L. Hyrich, Imogen Bolger, Shoop-Worrall, Stephanie JW [0000-0002-9441-5535], Hyrich, Kimme L [0000-0001-8242-9262], and Apollo - University of Cambridge Repository

Subjects
Decision support system, Health Personnel, Immunology, Early juvenile, Nothing About Us Without Us, Machine learning, computer.software_genre, General Biochemistry, Genetics and Molecular Biology, Machine Learning, 03 medical and health sciences, Viewpoint, 0302 clinical medicine, Rheumatology, Stakeholder Participation, Health care, Selection (linguistics), Immunology and Allergy, Medicine, Humans, 030212 general & internal medicine, outcome assessment, Health professionals, business.industry, Interpretation (philosophy), outcome and process assessment, health care, Patient Outcome Assessment, juvenile, arthritis, patient reported outcome measures, Key (cryptography), epidemiology, Artificial intelligence, Patient Participation, business, computer, 030217 neurology & neurosurgery, Unsupervised Machine Learning
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.

Published
2021

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