Machine Learning Analysis Applied to Prediction of Early Progression Independent of Relapse Activity in Multiple Sclerosis Patients
Our results showed the feasibility of applying ML techniques to predict short-term PIRA in newly diagnosed pwMS by using routine clinical practice data, paving the way for tailored and personalized approaches.
Authors
- Venet Osmani (ORCID: https://orcid.org/0000-0001-7306-2972)
- Sabrina Marangoni
- Matteo Betti (ORCID: https://orcid.org/0009-0008-8458-3747)
- Bruno Giometto (ORCID: https://orcid.org/0000-0003-2311-9752)
- Emilio Portaccio (ORCID: https://orcid.org/0000-0002-9662-1762)
- Antonio Uccelli (ORCID: https://orcid.org/0000-0002-2008-6038)
- Maria Pia Amato (ORCID: https://orcid.org/0000-0003-3325-3760)
- Monica Moroni (ORCID: https://orcid.org/0000-0003-1852-7217)
- Stefano Bovo (ORCID: https://orcid.org/0000-0002-0278-6786)
- Lorenzo Gios (ORCID: https://orcid.org/0000-0003-2981-0851)
- Angelo Bellinvia (ORCID: https://orcid.org/0000-0001-9889-9058)
- Flavio Ragni (ORCID: https://orcid.org/0000-0001-7767-7288)
- Matilde Inglese (ORCID: https://orcid.org/0000-0002-9610-0297)
- Caterina Lapucci (ORCID: https://orcid.org/0000-0002-3527-6520)
- Marco Chierici (ORCID: https://orcid.org/0000-0001-9791-9301)
- Manuela Marenco (ORCID: https://orcid.org/0000-0003-4328-8000)
- Walter Endrizzi
- Valentina Poretto (ORCID: https://orcid.org/0000-0001-6517-0578)
- Chiara Longo (ORCID: https://orcid.org/0000-0001-6904-6494)
- Giuseppe Jurman (ORCID: https://orcid.org/0000-0002-2705-5728)
Institutions
- Humanitas University (IT)
- University of Trento (IT)
- Fondazione Bruno Kessler (IT)
- Provincia Autonoma di Trento (IT)
- Don Carlo Gnocchi Foundation (IT)
- Ospedale Policlinico San Martino (IT)
- University of Florence (IT)
- University of Genoa (IT)
Publication Details
- Journal
- European Journal of Neurology
- Published
- 2025-11-28
- DOI
- https://doi.org/10.1111/ene.70417
- Citations
- 1
- Primary Topic
- Multiple Sclerosis Research Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.47
Funders
- Ministero della Salute