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"Longitudinal machine learning modeling of MS patient trajectories improves ..."

Edward De Brouwer et al. (2021)

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DOI: 10.1016/J.CMPB.2021.106180

access: closed

type: Journal Article

metadata version: 2023-09-30

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  - journals/cmpb/BrouwerBMHTEOOG21
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  - https://dblp.org/rec/journals/cmpb/BrouwerBMHTEOOG21
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 Trivio, 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)

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