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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...
| Autores principales: | , , , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Frontiers Media S.A.
2023
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| Materias: | |
| 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 |