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.

翻译：临床医学中许多关注的变量（如疾病严重程度）通过离散的有序类别（如正常/轻度/中度/重度）进行记录。这些标签被用于训练和评估疾病严重程度预测模型。然而，有序类别本质上是底层连续严重程度谱的简化表示。相较于有序类别，连续评分在检测疾病严重程度随时间变化的微小差异时更为敏感。本文提出一种通用框架，仅利用模型开发过程中的离散有序标签即可准确预测连续值变量。研究发现，在三个临床预测任务中，考虑训练标签有序关系的模型优于传统多类分类模型。特别是，与多类分类模型相比，有序分类和回归模型生成的连续评分与专家对疾病严重程度的排序具有显著更高的相关性，且均方误差更低。此外，MC dropout的使用显著提升了所有被评估深度学习方法预测连续评分的能力，生成的评分能够真实反映底层的连续目标变量。我们证明了即使在模型开发仅涉及离散有序标签的情况下，也能生成准确的连续值预测。该新型框架已在三项不同临床预测任务中得到验证，并证实可弥合离散有序标签与底层连续值变量之间的差距。