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].

De Brouwer, EdwardBecker, ThijsMoreau, YvesHavrdova, Eva KubalaTrojano, MariaEichau, SaraOzakbas, SerkanOnofrj, MarcoGrammond, PierreKuhle, JensKappos, LudwigSola, PatriziaCartechini, ElisabettaLechner-Scott, JeannetteAlroughani, RaedGerlach, OliverKalincik, TomasGranella, FrancoGrand'Maison, FrancoisBergamaschi, RobertoSá, Maria JoséVan Wijmeersch, BartSoysal, AysunSanchez-Menoyo, Jose LuisSolaro, ClaudioBoz, CavitIuliano, GerardoBuzzard, KatherineAguera-Morales, EduardoTerzi, MuratTrivio, Tamara CastilloSpitaleri, DanieleVan Pesch, VincentShaygannejad, VahidMoore, FraserOreja-Guevara, CeliaMaimone, DavideGouider, RiadhCsepany, TundeRamo-Tello, CristinaPeeters, Liesbet2023-05-032023-05-032021-11-05http://hdl.handle.net/10668/22137enCorrigendum 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].research article34749246open access10.1016/j.cmpb.2021.1064791872-7565https://documentserver.uhasselt.be//bitstream/1942/36136/1/1-s2.0-S0169260721005538-main.pdf