HYPE: A High Performing NLP System for Automatically Detecting Hypoglycemia Events from Electronic Health Record Notes
This work addresses hypoglycemia detection for diabetes patients using EHR data, representing an incremental application of existing methods to a specific medical domain.
The researchers tackled the problem of automatically detecting hypoglycemia events from electronic health record notes by developing a deep learning-based NLP system called HYPE, which achieved an F1 score of 0.91 in cross-validation. This system aims to improve surveillance and facilitate timely treatment for diabetes patients.
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.