2018/11/29 by Yonghao Jin, Fei Li, Jin, Yonghao +3
Computer Science · Health Professions · Medicine · #Artificial Intelligence (cs.AI) #Artificial Intelligence in Healthcare #Computation and Language (cs.CL) #Diabetes Management and Research #FOS: Computer and information sciences #Machine Learning in Healthcare #cs.AI #cs.CL
paper · pdf · doi:10.48550/arxiv.1811.11945
Machine Learning for Health (ML4H) Workshop at NeurIPS 2018 arXiv:1811.07216
arxiv created 2018/11/29 · openalex publication_date 2018/11/29 · arxiv updated 2018/11/30 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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 ± 0.03, Recall=0.86 ± 0.03, F1=0.91 ± 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.