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].

Sat, 01 Jan 2022 00:00:00 +0100

Edward De Brouwer, Thijs Becker, Yves Moreau , Eva Kubala Havrdova , Maria Trojano, Sara Eichau, Serkan Ozakbas, Marco Onofrj, Pierre Grammond, Jens Kuhle , Ludwig Kappos , Patrizia Sola, Elisabetta Cartechini, Jeannette Lechner-Scott , Raed Alroughani, Oliver Gerlach, Tomas Kalincik, Franco Granella, Francois Grand'Maison, Roberto Bergamaschi, Maria Jose Sa , Bart Van Wijmeersch , Aysun Soysal, Jose Luis Sanchez-Menoyo, Claudio Solaro, Cavit Boz, Gerardo Iuliano, Katherine Buzzard, Eduardo Aguera-Morales , Murat Terzi, Tamara Castillo Trivino, Daniele Spitaleri, Vincent Van Pesch , Vahid Shaygannejad, Fraser Moore, Celia Oreja Guevara, Davide Maimone , Riadh Gouider , Tunde Csepany, Cristina Ramo-Tello , Liesbet M. Peeters:
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]. Comput. Methods Programs Biomed. 213: 106479 (2022)