HYPE: A High Performing NLP System for Automatically Detecting Hypoglycemia Events from Electronic Health Record Notes

TL;DR

This paper presents HYPE, a deep learning NLP system that automatically detects hypoglycemia events from electronic health record notes, demonstrating high accuracy and potential for improving patient care and surveillance.

Contribution

The study introduces HYPE, a CNN-based NLP system trained on expert-annotated EHR notes, achieving high performance in hypoglycemia detection despite data imbalance.

Findings

Precision of 0.96 in hypoglycemia detection

Recall of 0.86 indicating effective sensitivity

High F1 score of 0.91 demonstrating balanced performance

Abstract

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.