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.

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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

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article

Machine Learning Analysis Applied to Prediction of Early Progression Independent of Relapse Activity in Multiple Sclerosis Patients

Venet Osmani, Sabrina Marangoni, Matteo Betti, Bruno Giometto et al.
1 citations
European Journal of Neurology
Multiple Sclerosis Research Studies
1.47
article

Machine Learning Analysis Applied to Prediction of Early Progression Independent of Relapse Activity in Multiple Sclerosis Patients

Venet Osmani, Sabrina Marangoni, Matteo Betti, Bruno Giometto, Emilio Portaccio, Antonio Uccelli, Maria Pia Amato, Monica Moroni, Stefano Bovo, Lorenzo Gios, Angelo Bellinvia, Flavio Ragni, Matilde Inglese, Caterina Lapucci, Marco Chierici, Manuela Marenco, Walter Endrizzi, Valentina Poretto, Chiara Longo, Giuseppe Jurman
article en
1 citations

Abstract

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.

European Journal of NeurologyVol. 32(12)
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)
Ministero della Salute
Openalex Percentile: Top 13%
Multiple Sclerosis Research Studies
1.47
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