Machine-learning-based prediction of disability progression in multiple sclerosis: An observational, international, multi-center study.
De Brouwer E, Becker T, Werthen-Brabants L, Dewulf P, Iliadis D, Dekeyser C, Laureys G, Van Wijmeersch B, Popescu V, Dhaene T, Deschrijver D, Waegeman W, De Baets B, Stock M, Horakova D, Patti F, Izquierdo G, Eichau S, Girard M, Prat A, Lugaresi A, Grammond P, Kalincik T, Alroughani R, Grand'Maison F, Skibina O, Terzi M, Lechner-Scott J, Gerlach O, Khoury SJ, Cartechini E, Van Pesch V, Sà MJ, Weinstock-Guttman B, Blanco Y, Ampapa R, Spitaleri D, Solaro C, Maimone D, Soysal A, Iuliano G, Gouider R, Castillo-Triviño T, Sánchez-Menoyo JL, Laureys G, van der Walt A, Oh J, Aguera-Morales E, Altintas A, Al-Asmi A, de Gans K, Fragoso Y, Csepany T, Hodgkinson S, Deri N, Al-Harbi T, Taylor B, Gray O, Lalive P, Rozsa C, McGuigan C, Kermode A, Sempere AP, Mihaela S, Simo M, Hardy T, Decoo D, Hughes S, Grigoriadis N, Sas A, Vella N, Moreau Y, Peeters L.
De Brouwer E, et al. Among authors: sas a.
PLOS Digit Health. 2024 Jul 25;3(7):e0000533. doi: 10.1371/journal.pdig.0000533. eCollection 2024 Jul.
PLOS Digit Health. 2024.
PMID: 39052668
Free PMC article.
