  Hypoglycemia is common and potentially dangerous among those treated for
diabetes. Electronic health records (EHRs) are important resources for
hypoglycemia surveillance. In this study, we report the development and
evaluation of deep learning-based natural language processing systems to
automatically detect hypoglycemia events from the EHR narratives. Experts in
Public Health annotated 500 EHR notes from patients with diabetes. We used this
annotated dataset to train and evaluate HYPE, supervised NLP systems for
hypoglycemia detection. In our experiment, the convolutional neural network
model yielded promising performance $Precision=0.96 \pm 0.03, Recall=0.86 \pm
0.03, F1=0.91 \pm 0.03$ in a 10-fold cross-validation setting. Despite the
annotated data is highly imbalanced, our CNN-based HYPE system still achieved a
high performance for hypoglycemia detection. HYPE could be used for EHR-based
hypoglycemia surveillance and to facilitate clinicians for timely treatment of
high-risk patients.
