UMMS Affiliation

Department of Medicine

Publication Date

2019-11-08

Document Type

Article

Disciplines

Analytical, Diagnostic and Therapeutic Techniques and Equipment | Artificial Intelligence and Robotics | Endocrine System Diseases | Health Information Technology | Nutritional and Metabolic Diseases

Abstract

BACKGROUND: Hypoglycemic events are common and potentially dangerous conditions among patients being treated for diabetes. Automatic detection of such events could improve patient care and is valuable in population studies. Electronic health records (EHRs) are valuable resources for the detection of such events.

OBJECTIVE: In this study, we aim to develop a deep-learning-based natural language processing (NLP) system to automatically detect hypoglycemic events from EHR notes. Our model is called the High-Performing System for Automatically Detecting Hypoglycemic Events (HYPE).

METHODS: Domain experts reviewed 500 EHR notes of diabetes patients to determine whether each sentence contained a hypoglycemic event or not. We used this annotated corpus to train and evaluate HYPE, the high-performance NLP system for hypoglycemia detection. We built and evaluated both a classical machine learning model (ie, support vector machines [SVMs]) and state-of-the-art neural network models.

RESULTS: We found that neural network models outperformed the SVM model. The convolutional neural network (CNN) model yielded the highest performance in a 10-fold cross-validation setting: mean precision=0.96 (SD 0.03), mean recall=0.86 (SD 0.03), and mean F1=0.91 (SD 0.03).

CONCLUSIONS: Despite the challenges posed by small and highly imbalanced data, our CNN-based HYPE system still achieved a high performance for hypoglycemia detection. HYPE can be used for EHR-based hypoglycemia surveillance and population studies in diabetes patients.

Keywords

adverse events, convolutional neural networks, hypoglycemia, natural language processing

Rights and Permissions

© Yonghao Jin, Fei Li, Varsha G Vimalananda, Hong Yu. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 10.11.2019. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on http://medinform.jmir.org/, as well as this copyright and license information must be included.

DOI of Published Version

10.2196/14340

Source

JMIR Med Inform. 2019 Nov 8;7(4):e14340. doi: 10.2196/14340. Link to article on publisher's site

Journal/Book/Conference Title

JMIR medical informatics

Related Resources

Link to Article in PubMed

PubMed ID

31702562

Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.

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