Machine learning classifier to identify clinical and radiological features relevant to disability progression in multiple sclerosis

OBJECTIVES: To evaluate the accuracy of a data-driven approach, such as machine learning classification, in predicting disability progression in MS. METHODS: We analyzed structural brain images of 163 subjects diagnosed with MS acquired at two different sites. Participants were followed up for 2–6 y...

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Detalles Bibliográficos
Autores principales: Tommasin, Silvia, Cocozza, Sirio, Taloni, Alessandro, Giannì, Costanza, Petsas, Nikolaos, Pontillo, Giuseppe, Petracca, Maria, Ruggieri, Serena, De Giglio, Laura, Pozzilli, Carlo, Brunetti, Arturo, Pantano, Patrizia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Berlin Heidelberg 2021
Materias:
Original Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563671/
https://www.ncbi.nlm.nih.gov/pubmed/33970338
http://dx.doi.org/10.1007/s00415-021-10605-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8563671/
https://www.ncbi.nlm.nih.gov/pubmed/33970338
http://dx.doi.org/10.1007/s00415-021-10605-7

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