低血糖
学习迁移
深度学习
人工智能
1型糖尿病
医学
卷积神经网络
2型糖尿病
糖尿病
曲线下面积
胰岛素
接收机工作特性
机器学习
数据集
连续血糖监测
计算机科学
内科学
内分泌学
作者
Helene B. Thomsen,M. K. Jakobsen,Nikolaj Hecht-Pedersen,Morten Hasselstrøm Jensen,Thomas Kronborg
标识
DOI:10.1177/19322968231215324
摘要
Background: Hypoglycemia is common in insulin-treated type 2 diabetes (T2D) patients, which can lead to decreased quality of life or premature death. Deep learning models offer promise of accurate predictions, but data scarcity poses a challenge. This study aims to develop a deep learning model utilizing transfer learning to predict hypoglycemia. Methods: Continuous glucose monitoring (CGM) data from 226 patients with type 1 diabetes (T1D) and 180 patients with T2D were utilized. Data were structured into one-hour samples and labeled as hypoglycemia or not depending on whether three consecutive CGM values were below 3.9 [mmol/L] (70 mg/dL) one hour after the sample. A convolutional neural network (CNN) was pre-trained with the T1D data set and subsequently fitted using a T2D data set, all while being optimized toward maximizing the area under the receiver operating characteristics curve (AUC) value, and it was externally validated on a separate T2D data set. Results: The developed model was externally validated with 334 711 one-hour CGM samples, of which 15 695 (4.69%) were labeled as hypoglycemic. The model achieved an AUC of 0.941 and a positive predictive value of 40.49% at a specificity of 95% and a sensitivity of 69.16%. Conclusions: The transfer learned CNN model showed promising performance in predicting hypoglycemic episodes and with slightly better results than a non-transfer learned CNN model.
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