The hidden information in patient-reported outcomes and clinician-assessed outcomes: multiple sclerosis as a proof of concept of a machine learning approach

Machine learning (ML) applied to patient-reported (PROs) and clinical-assessed outcomes (CAOs) could favour a more predictive and personalized medicine. Our aim was to confirm the important role of applying ML to PROs and CAOs of people with relapsing-remitting (RR) and secondary progressive (SP) fo...

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Detalles Bibliográficos
Autores principales: Brichetto, Giampaolo, Monti Bragadin, Margherita, Fiorini, Samuele, Battaglia, Mario Alberto, Konrad, Giovanna, Ponzio, Michela, Pedullà, Ludovico, Verri, Alessandro, Barla, Annalisa, Tacchino, Andrea
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Brief Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7005074/
https://www.ncbi.nlm.nih.gov/pubmed/31659583
http://dx.doi.org/10.1007/s10072-019-04093-x

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7005074/
https://www.ncbi.nlm.nih.gov/pubmed/31659583
http://dx.doi.org/10.1007/s10072-019-04093-x

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