دورية أكاديمية
Generalization of a Deep Learning Model for Continuous Glucose Monitoring–Based Hypoglycemia Prediction: Algorithm Development and Validation Study
| العنوان: | Generalization of a Deep Learning Model for Continuous Glucose Monitoring–Based Hypoglycemia Prediction: Algorithm Development and Validation Study |
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| المؤلفون: | Jian Shao, Ying Pan, Wei-Bin Kou, Huyi Feng, Yu Zhao, Kaixin Zhou, Shao Zhong |
| المصدر: | JMIR Medical Informatics, Vol 12, Pp e56909-e56909 (2024) |
| بيانات النشر: | JMIR Publications, 2024. |
| سنة النشر: | 2024 |
| المجموعة: | LCC:Computer applications to medicine. Medical informatics |
| مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7 |
| الوصف: | Abstract BackgroundPredicting hypoglycemia while maintaining a low false alarm rate is a challenge for the wide adoption of continuous glucose monitoring (CGM) devices in diabetes management. One small study suggested that a deep learning model based on the long short-term memory (LSTM) network had better performance in hypoglycemia prediction than traditional machine learning algorithms in European patients with type 1 diabetes. However, given that many well-recognized deep learning models perform poorly outside the training setting, it remains unclear whether the LSTM model could be generalized to different populations or patients with other diabetes subtypes. ObjectiveThe aim of this study was to validate LSTM hypoglycemia prediction models in more diverse populations and across a wide spectrum of patients with different subtypes of diabetes. MethodsWe assembled two large data sets of patients with type 1 and type 2 diabetes. The primary data set including CGM data from 192 Chinese patients with diabetes was used to develop the LSTM, support vector machine (SVM), and random forest (RF) models for hypoglycemia prediction with a prediction horizon of 30 minutes. Hypoglycemia was categorized into mild (glucose=54-70 mg/dL) and severe (glucose |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 2291-9694 58107045 |
| العلاقة: | https://medinform.jmir.org/2024/1/e56909Test; https://doaj.org/toc/2291-9694Test |
| DOI: | 10.2196/56909 |
| الوصول الحر: | https://doaj.org/article/0bb75f58107045479f99003815675e9fTest |
| رقم الانضمام: | edsdoj.0bb75f58107045479f99003815675e9f |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 22919694 58107045 |
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| DOI: | 10.2196/56909 |