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Many variables of interest in clinical medicine, like disease severity, arerecorded using discrete ordinal categories such as normal/mild/moderate/severe.These labels are used to train and evaluate disease severity prediction models.However, ordinal categories represent a simplification of an underlyingcontinuous severity spectrum. Using continuous scores instead of ordinalcategories is more sensitive to detecting small changes in disease severityover time. Here, we present a generalized framework that accurately predictscontinuously valued variables using only discrete ordinal labels during modeldevelopment. We found that for three clinical prediction tasks, models thattake the ordinal relationship of the training labels into account outperformedconventional multi-class classification models. Particularly the continuousscores generated by ordinal classification and regression models showed asignificantly higher correlation with expert rankings of disease severity andlower mean squared errors compared to the multi-class classification models.Furthermore, the use of MC dropout significantly improved the ability of allevaluated deep learning approaches to predict continuously valued scores thattruthfully reflect the underlying continuous target variable. We showed thataccurate continuously valued predictions can be generated even if the modeldevelopment only involves discrete ordinal labels. The novel framework has beenvalidated on three different clinical prediction tasks and has proven to bridgethe gap between discrete ordinal labels and the underlying continuously valuedvariables.

A generalized framework to predict continuous scores from medical ordinal labels