Accounting for uncertainty in training data to improve machine learning performance in predicting new disease activity in early multiple sclerosis

INTRODUCTION: Machine learning (ML) has great potential for using health data to predict clinical outcomes in individual patients. Missing data are a common challenge in training ML algorithms, such as when subjects withdraw from a clinical study, leaving some samples with missing outcome labels. In...

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Detalles Bibliográficos
Autores principales: Tayyab, Maryam, Metz, Luanne M., Li, David K.B., Kolind, Shannon, Carruthers, Robert, Traboulsee, Anthony, Tam, Roger C.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Neurology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10251494/
https://www.ncbi.nlm.nih.gov/pubmed/37305756
http://dx.doi.org/10.3389/fneur.2023.1165267

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10251494/
https://www.ncbi.nlm.nih.gov/pubmed/37305756
http://dx.doi.org/10.3389/fneur.2023.1165267

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