  Electronic health records (EHR) are increasingly being used for constructing
disease risk prediction models. Feature engineering in EHR data however is
challenging due to their highly dimensional and heterogeneous nature.
Low-dimensional representations of EHR data can potentially mitigate these
challenges. In this paper, we use global vectors (GloVe) to learn word
embeddings for diagnoses and procedures recorded using 13 million ontology
terms across 2.7 million hospitalisations in national UK EHR. We demonstrate
the utility of these embeddings by evaluating their performance in identifying
patients which are at higher risk of being hospitalised for congestive heart
failure. Our findings indicate that embeddings can enable the creation of
robust EHR-derived disease risk prediction models and address some the
limitations associated with manual clinical feature engineering.
