Personalized federated learning for predicting disability progression in multiple sclerosis using real-world routine clinical data.
Pirmani A, De Brouwer E, Arany Á, Oldenhof M, Passemiers A, Faes A, Kalincik T, Ozakbas S, Gouider R, Willekens B, Horakova D, Havrdova EK, Patti F, Prat A, Lugaresi A, Tomassini V, Grammond P, Cartechini E, Roos I, Boz C, Alroughani R, Amato MP, Buzzard K, Lechner-Scott J, Guimarães J, Solaro C, Gerlach O, Soysal A, Kuhle J, Sanchez-Menoyo JL, Spitaleri D, Csepany T, Van Wijmeersch B, Ampapa R, Prevost J, Khoury SJ, Van Pesch V, John N, Maimone D, Weinstock-Guttman B, Laureys G, McCombe P, Blanco Y, Altintas A, Al-Asmi A, Garber J, Van der Walt A, Butzkueven H, de Gans K, Rozsa C, Taylor B, Al-Harbi T, Sas A, Rajda C, Gray O, Decoo D, Carroll WM, Kermode AG, Fabis-Pedrini M, Mason D, Perez-Sempere A, Simu M, Shuey N, Singhal B, Cauchi M, Hardy TA, Ramanathan S, Lalive P, Sirbu CA, Hughes S, Castillo Trivino T, Peeters LM, Moreau Y.
Pirmani A, et al. Among authors: cauchi m.
NPJ Digit Med. 2025 Jul 24;8(1):478. doi: 10.1038/s41746-025-01788-8.
NPJ Digit Med. 2025.
PMID: 40707601
Free PMC article.