  Real-time prediction of clinical interventions remains a challenge within
intensive care units (ICUs). This task is complicated by data sources that are
noisy, sparse, heterogeneous and outcomes that are imbalanced. In this paper,
we integrate data from all available ICU sources (vitals, labs, notes,
demographics) and focus on learning rich representations of this data to
predict onset and weaning of multiple invasive interventions. In particular, we
compare both long short-term memory networks (LSTM) and convolutional neural
networks (CNN) for prediction of five intervention tasks: invasive ventilation,
non-invasive ventilation, vasopressors, colloid boluses, and crystalloid
boluses. Our predictions are done in a forward-facing manner to enable
"real-time" performance, and predictions are made with a six hour gap time to
support clinically actionable planning. We achieve state-of-the-art results on
our predictive tasks using deep architectures. We explore the use of feature
occlusion to interpret LSTM models, and compare this to the interpretability
gained from examining inputs that maximally activate CNN outputs. We show that
our models are able to significantly outperform baselines in intervention
prediction, and provide insight into model learning, which is crucial for the
adoption of such models in practice.
