Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression.

Fri, 01 Jan 2021 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 :
Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression. Comput. Methods Programs Biomed. 208: 106180 (2021)