TY  - JOUR
AU  - De-Brouwer, Edward
AU  - Becker, Thijs
AU  - Moreau, Yves
AU  - Havrdova, Eva Kubala
AU  - Trojano, Maria
AU  - Eichau, Sara
AU  - Ozakbas, Serkan
AU  - Onofrj, Marco
AU  - Grammond, Pierre
AU  - Kuhle, Jens
AU  - Kappos, Ludwig
AU  - Sola, Patrizia
AU  - Cartechini, Elisabetta
AU  - Lechner-Scott, Jeannette
AU  - Alroughani, Raed
AU  - Gerlach, Oliver
AU  - Kalincik, Tomas
AU  - Granella, Franco
AU  - Grand'Maison, Francois
AU  - Bergamaschi, Roberto
AU  - Sa, Maria Jose
AU  - Van-Wijmeersch, Bart
AU  - Soysal, Aysun
AU  - Sanchez-Menoyo, Jose Luis
AU  - Solaro, Claudio
AU  - Boz, Cavit
AU  - Iuliano, Gerardo
AU  - Buzzard, Katherine
AU  - Aguera-Morales, Eduardo
AU  - Terzi, Murat
AU  - Trivio, Tamara Castillo
AU  - Spitaleri, Daniele
AU  - Van-Pesch, Vincent
AU  - Shaygannejad, Vahid
AU  - Moore, Fraser
AU  - Oreja-Guevara, Celia
AU  - Maimone, Davide
AU  - Gouider, Riadh
AU  - Csepany, Tunde
AU  - Ramo-Tello, Cristina
AU  - Peeters, Liesbet
PY  - 2021
DO  - 10.1016/j.cmpb.2021.106479
UR  - http://hdl.handle.net/10668/22137
T2  - Computer methods and programs in biomedicine
AB  - Background and objectives: Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more...
LA  - en
PB  - Elsevier
KW  - Disability progression
KW  - Electronic health records
KW  - Longitudinal data
KW  - Multiple sclerosis
KW  - Real-world data
KW  - Recurrent neural networks
KW  - Precision Medicine
KW  - Neural Networks, Computer
KW  - Machine Learning
KW  - Disease Progression
KW  - Information Sources
TI  - Longitudinal machine learning modeling of MS patient trajectories improves predictions of disability progression: [Computer Methods and Programs in Biomedicine, Volume 208, (September 2021) 106180].
TY  - research article
VL  - 213
ER  -