  The Intensive Care Unit (ICU) is one of the most important parts of a
hospital, which admits critically ill patients and provides continuous
monitoring and treatment. Various patient outcome prediction methods have been
attempted to assist healthcare professionals in clinical decision-making.
Existing methods focus on measuring the similarity between patients using deep
neural networks to capture the hidden feature structures. However, the
higher-order relationships are ignored, such as patient characteristics (e.g.,
diagnosis codes) and their causal effects on downstream clinical predictions.
In this paper, we propose a novel Hypergraph Convolutional Network that allows
the representation of non-pairwise relationships among diagnosis codes in a
hypergraph to capture the hidden feature structures so that fine-grained
patient similarity can be calculated for personalized mortality risk
prediction. Evaluation using a publicly available eICU Collaborative Research
Database indicates that our method achieves superior performance over the
state-of-the-art models on mortality risk prediction. Moreover, the results of
several case studies demonstrated the effectiveness and robustness of the model
decisions.

Machine Learning