Cargando…
Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis
Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relap...
Autores principales: | Seccia, Ruggiero, Gammelli, Daniele, Dominici, Fabio, Romano, Silvia, Landi, Anna Chiara, Salvetti, Marco, Tacchella, Andrea, Zaccaria, Andrea, Crisanti, Andrea, Grassi, Francesca, Palagi, Laura |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7083323/ https://www.ncbi.nlm.nih.gov/pubmed/32196512 http://dx.doi.org/10.1371/journal.pone.0230219 |
Ejemplares similares
-
Machine Learning Use for Prognostic Purposes in Multiple Sclerosis
por: Seccia, Ruggiero, et al.
Publicado: (2021) -
Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study
por: Tacchella, Andrea, et al.
Publicado: (2018) -
Product progression: a machine learning approach to forecasting industrial upgrading
por: Albora, Giambattista, et al.
Publicado: (2023) -
Noise in multiple sclerosis: unwanted and necessary
por: Bordi, Isabella, et al.
Publicado: (2014) -
How the Taxonomy of Products Drives the Economic Development of Countries
por: Zaccaria, Andrea, et al.
Publicado: (2014)