  We introduce a novel contrastive representation learning objective and a
training scheme for clinical time series. Specifically, we project high
dimensional EHR. data to a closed unit ball of low dimension, encoding
geometric priors so that the origin represents an idealized perfect health
state and the Euclidean norm is associated with the patient's mortality risk.
Moreover, using septic patients as an example, we show how we could learn to
associate the angle between two vectors with the different organ system
failures, thereby, learning a compact representation which is indicative of
both mortality risk and specific organ failure. We show how the learned
embedding can be used for online patient monitoring, can supplement clinicians
and improve performance of downstream machine learning tasks. This work was
partially motivated from the desire and the need to introduce a systematic way
of defining intermediate rewards for Reinforcement Learning in critical care
medicine. Hence, we also show how such a design in terms of the learned
embedding can result in qualitatively different policies and value
distributions, as compared with using only terminal rewards.
