A generalized framework to predict continuous scores from medical
  ordinal labels
Katharina V. Hoebel Andreanne Lemay John Peter Campbell Susan Ostmo Michael F. Chiang Christopher P. Bridge Matthew D. Li Praveer Singh Aaron S. Coyner Jayashree Kalpathy-Cramer
Many variables of interest in clinical medicine, like disease severity, are
recorded 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 underlying
continuous severity spectrum. Using continuous scores instead of ordinal
categories is more sensitive to detecting small changes in disease severity
over time. Here, we present a generalized framework that accurately predicts
continuously valued variables using only discrete ordinal labels during model
development. We found that for three clinical prediction tasks, models that
take the ordinal relationship of the training labels into account outperformed
conventional multi-class classification models. Particularly the continuous
scores generated by ordinal classification and regression models showed a
significantly higher correlation with expert rankings of disease severity and
lower mean squared errors compared to the multi-class classification models.
Furthermore, the use of MC dropout significantly improved the ability of all
evaluated deep learning approaches to predict continuously valued scores that
truthfully reflect the underlying continuous target variable. We showed that
accurate continuously valued predictions can be generated even if the model
development only involves discrete ordinal labels. The novel framework has been
validated on three different clinical prediction tasks and has proven to bridge
the gap between discrete ordinal labels and the underlying continuously valued
variables.