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This study compares supervised and semi-supervised learning methods to predict which rheumatoid arthritis patients will require step-up therapy within a year. The results indicate that semi-supervised learning outperforms supervised learning, achieving a higher F-measure and better precision and recall. The findings suggest that traditional supervised approaches may not be optimal for predicting step-up therapy due to inaccurate negative labels in the data.

Author

ogermanleonardoac

Language

EN