Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/36136
Title: 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]
Authors: DE BROUWER, Edward 
BECKER, Thijs 
Moreau, Yves
Havrdova, Eva Kubala
Trojano, Maria
Eichau, Sara
Ozakbas, Serkan
Onofrj, Marco
Grammond, Pierre
Kuhle, Jens
Kappos, Ludwig
Sola, Patrizia
Cartechini, Elisabetta
Lechner-Scott, Jeannette
Alroughani, Raed
Gerlach, Oliver
Kalincik, Tomas
Granella, Franco
Grand'Maison, Francois
VAN WIJMEERSCH, Bart 
Bergamaschi, Roberto
Sá, Maria José
Soysal, Aysun
Sanchez-Menoyo, Jose Luis
Solaro, Claudio
Boz, Cavit
Iuliano, Gerardo
Buzzard, Katherine
Aguera-Morales, Eduardo
Terzi, Murat
Trivio, Tamara Castillo
Spitaleri, Daniele
Van Pesch, Vincent
Shaygannejad, Vahid
Moore, Fraser
Oreja-Guevara, Celia
Maimone, Davide
Gouider, Riadh
Csepany, Tunde
Ramo-Tello, Cristina
PEETERS, Liesbet 
Issue Date: 2022
Publisher: ELSEVIER IRELAND LTD
Source: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, 213 , (Art N° 106479)
Notes: De Brouwer, E (corresponding author), Katholieke Univ Leuven, ESAT STADIUS, B-3001 Leuven, Belgium.
Edward.debrouwer@esat.kuleuven.be
Document URI: http://hdl.handle.net/1942/36136
ISSN: 0169-2607
e-ISSN: 1872-7565
DOI: 10.1016/j.cmpb.2021.106479
Category: A1
Type: Journal Contribution
Appears in Collections:Research publications

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