  Early recognition of risky trajectories during an Intensive Care Unit (ICU)
stay is one of the key steps towards improving patient survival. Learning
trajectories from physiological signals continuously measured during an ICU
stay requires learning time-series features that are robust and discriminative
across diverse patient populations. Patients within different ICU populations
(referred here as domains) vary by age, conditions and interventions. Thus,
mortality prediction models using patient data from a particular ICU population
may perform suboptimally in other populations because the features used to
train such models have different distributions across the groups. In this
paper, we explore domain adaptation strategies in order to learn mortality
prediction models that extract and transfer complex temporal features from
multivariate time-series ICU data. Features are extracted in a way that the
state of the patient in a certain time depends on the previous state. This
enables dynamic predictions and creates a mortality risk space that describes
the risk of a patient at a particular time. Experiments based on cross-ICU
populations reveals that our model outperforms all considered baselines. Gains
in terms of AUC range from 4% to 8% for early predictions when compared with a
recent state-of-the-art representative for ICU mortality prediction. In
particular, models for the Cardiac ICU population achieve AUC numbers as high
as 0.88, showing excellent clinical utility for early mortality prediction.
Finally, we present an explanation of factors contributing to the possible ICU
outcomes, so that our models can be used to complement clinical reasoning.
