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.

本文旨在利用所有可用的 ICU 数据（生命体征、实验室数据、病历及患者基本信息）并关注学习数据的丰富表示，通过使用递归神经网络 LSTM 和卷积神经网络 CNN 分别预测侵入性干预的发生和停止。该预测支持 “实时” 表现，并在六小时内进行以支持临床可操作的规划，同时通过特征遮盖等方法提高模型的解释性，最终超过对照组并提供模型学习洞见。