%0 Journal Article
%A De Brouwer, Edward
%A Becker, Thijs
%A Moreau, Yves
%A Havrdova, Eva Kubala
%A Trojano, Maria
%A Eichau, Sara
%A Ozakbas, Serkan
%A Onofrj, Marco
%A Grammond, Pierre
%A Kuhle, Jens
%A Kappos, Ludwig
%A Sola, Patrizia
%A Cartechini, Elisabetta
%A Lechner-Scott, Jeannette
%A Alroughani, Raed
%A Gerlach, Oliver
%A Kalincik, Tomas
%A Granella, Franco
%A Grand'Maison, Francois
%A Bergamaschi, Roberto
%A Sá, Maria José
%A Van Wijmeersch, Bart
%A Soysal, Aysun
%A Sanchez-Menoyo, Jose Luis
%A Solaro, Claudio
%A Boz, Cavit
%A Iuliano, Gerardo
%A Buzzard, Katherine
%A Aguera-Morales, Eduardo
%A Terzi, Murat
%A Trivio, Tamara Castillo
%A Spitaleri, Daniele
%A Van Pesch, Vincent
%A Shaygannejad, Vahid
%A Moore, Fraser
%A Oreja-Guevara, Celia
%A Maimone, Davide
%A Gouider, Riadh
%A Csepany, Tunde
%A Ramo-Tello, Cristina
%A Peeters, Liesbet
%T Corrigendum to Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression: [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180].
%D 2021
%U http://hdl.handle.net/10668/22137
%~