Cargando…
Collaboration between a human group and artificial intelligence can improve prediction of multiple sclerosis course: a proof-of-principle study
Background: Multiple sclerosis has an extremely variable natural course. In most patients, disease starts with a relapsing-remitting (RR) phase, which proceeds to a secondary progressive (SP) form. The duration of the RR phase is hard to predict, and to date predictions on the rate of disease progre...
Autores principales: | Tacchella, Andrea, Romano, Silvia, Ferraldeschi, Michela, Salvetti, Marco, Zaccaria, Andrea, Crisanti, Andrea, Grassi, Francesca |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
F1000 Research Limited
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5990125/ https://www.ncbi.nlm.nih.gov/pubmed/29904574 http://dx.doi.org/10.12688/f1000research.13114.2 |
Ejemplares similares
-
Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis
por: Seccia, Ruggiero, et al.
Publicado: (2020) -
Machine Learning Use for Prognostic Purposes in Multiple Sclerosis
por: Seccia, Ruggiero, et al.
Publicado: (2021) -
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) -
Product progression: a machine learning approach to forecasting industrial upgrading
por: Albora, Giambattista, et al.
Publicado: (2023)