  This paper extends the class of ordinal regression models with a structured
interpretation of the problem by applying a novel treatment of encoded labels.
The net effect of this is to transform the underlying problem from an ordinal
regression task to a (structured) classification task which we solve with
conditional random fields, thereby achieving a coherent and probabilistic model
in which all model parameters are jointly learnt. Importantly, we show that
although we have cast ordinal regression to classification, our method still
fall within the class of decomposition methods in the ordinal regression
ontology. This is an important link since our experience is that many
applications of machine learning to healthcare ignores completely the important
nature of the label ordering, and hence these approaches should considered
naive in this ontology. We also show that our model is flexible both in how it
adapts to data manifolds and in terms of the operations that are available for
practitioner to execute. Our empirical evaluation demonstrates that the
proposed approach overwhelmingly produces superior and often statistically
significant results over baseline approaches on forty popular ordinal
regression models, and demonstrate that the proposed model significantly
out-performs baselines on synthetic and real datasets. Our implementation,
together with scripts to reproduce the results of this work, will be available
on a public GitHub repository.
