  Recent work on predicting patient outcomes in the Intensive Care Unit (ICU)
has focused heavily on the physiological time series data, largely ignoring
sparse data such as diagnoses and medications. When they are included, they are
usually concatenated in the late stages of a model, which may struggle to learn
from rarer disease patterns. Instead, we propose a strategy to exploit
diagnoses as relational information by connecting similar patients in a graph.
To this end, we propose LSTM-GNN for patient outcome prediction tasks: a hybrid
model combining Long Short-Term Memory networks (LSTMs) for extracting temporal
features and Graph Neural Networks (GNNs) for extracting the patient
neighbourhood information. We demonstrate that LSTM-GNNs outperform the
LSTM-only baseline on length of stay prediction tasks on the eICU database.
More generally, our results indicate that exploiting information from
neighbouring patient cases using graph neural networks is a promising research
direction, yielding tangible returns in supervised learning performance on
Electronic Health Records.
