Publication: Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases
Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases
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Trottet, C., Allam, A., Horvath, A. N., Finckh, A., Hügle, T., Adler, S., Kyburz, D., Micheroli, R., Krauthammer, M., & Ospelt, C. (2024). Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases. PLOS Digital Health, 3, e0000422. https://doi.org/10.1371/journal.pdig.0000422
Abstract
Abstract
Abstract
Analysing complex diseases such as chronic inflammatory joint diseases (CIJDs), where many factors influence the disease evolution over time, is a challenging task. CIJDs are rheumatic diseases that cause the immune system to attack healthy organs, mainly the joints. Different environmental, genetic and demographic factors affect disease development and progression. The Swiss Clinical Quality Management in Rheumatic Diseases (SCQM) Foundation maintains a national database of CIJDs documenting the disease management over time for 19'26
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Creators (Authors)
- Allam, Ahmed
- Horvath, Aron N
- Finckh, Axel
- Hügle, Thomas
- Adler, Sabine
- Kyburz, Diego
- Micheroli, Raphael
- Krauthammer, Michael
- Ospelt, Caroline
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Trottet, C., Allam, A., Horvath, A. N., Finckh, A., Hügle, T., Adler, S., Kyburz, D., Micheroli, R., Krauthammer, M., & Ospelt, C. (2024). Explainable deep learning for disease activity prediction in chronic inflammatory joint diseases. PLOS Digital Health, 3, e0000422. https://doi.org/10.1371/journal.pdig.0000422
