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We introduce a novel contrastive representation learning objective and atraining scheme for clinical time series. Specifically, we project highdimensional EHR. data to a closed unit ball of low dimension, encodinggeometric priors so that the origin represents an idealized perfect healthstate 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 toassociate the angle between two vectors with the different organ systemfailures, thereby, learning a compact representation which is indicative ofboth mortality risk and specific organ failure. We show how the learnedembedding can be used for online patient monitoring, can supplement cliniciansand improve performance of downstream machine learning tasks. This work waspartially motivated from the desire and the need to introduce a systematic wayof defining intermediate rewards for Reinforcement Learning in critical caremedicine. Hence, we also show how such a design in terms of the learnedembedding can result in qualitatively different policies and valuedistributions, as compared with using only terminal rewards.

Deep Normed Embeddings for Patient Representation