Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, UK.
Women's Centre, Oxford University Hospitals NHS Foundation Trust, Oxford OX3 9DU, UK.
Sensors (Basel). 2023 Sep 20;23(18):7990. doi: 10.3390/s23187990.
Gestational diabetes mellitus (GDM) is a subtype of diabetes that develops during pregnancy. Managing blood glucose (BG) within the healthy physiological range can reduce clinical complications for women with gestational diabetes. The objectives of this study are to (1) develop benchmark glucose prediction models with long short-term memory (LSTM) recurrent neural network models using time-series data collected from the GDm-Health platform, (2) compare the prediction accuracy with published results, and (3) suggest an optimized clinical review schedule with the potential to reduce the overall number of blood tests for mothers with stable and within-range glucose measurements. A total of 190,396 BG readings from 1110 patients were used for model development, validation and testing under three different prediction schemes: 7 days of BG readings to predict the next 7 or 14 days and 14 days to predict 14 days. Our results show that the optimized BG schedule based on a 7-day observational window to predict the BG of the next 14 days achieved the accuracies of the root mean square error (RMSE) = 0.958 ± 0.007, 0.876 ± 0.003, 0.898 ± 0.003, 0.622 ± 0.003, 0.814 ± 0.009 and 0.845 ± 0.005 for the after-breakfast, after-lunch, after-dinner, before-breakfast, before-lunch and before-dinner predictions, respectively. This is the first machine learning study that suggested an optimized blood glucose monitoring frequency, which is 7 days to monitor the next 14 days based on the accuracy of blood glucose prediction. Moreover, the accuracy of our proposed model based on the fingerstick blood glucose test is on par with the prediction accuracies compared with the benchmark performance of one-hour prediction models using continuous glucose monitoring (CGM) readings. In conclusion, the stacked LSTM model is a promising approach for capturing the patterns in time-series data, resulting in accurate predictions of BG levels. Using a deep learning model with routine fingerstick glucose collection is a promising, predictable and low-cost solution for BG monitoring for women with gestational diabetes.
妊娠期糖尿病（GDM）是一种在怀孕期间发生的糖尿病亚型。将血糖（BG）控制在健康的生理范围内可以降低患有妊娠糖尿病的女性的临床并发症。本研究的目的是：（1）使用来自 GDm-Health 平台收集的时间序列数据，通过长短期记忆（LSTM）递归神经网络模型开发基准血糖预测模型；（2）将预测准确性与已发表的结果进行比较；（3）通过优化临床复查方案，为血糖稳定且处于正常范围内的孕妇减少整体的血糖测试次数。共有 1110 名患者的 190396 个 BG 读数用于模型开发、验证和测试，共采用了三种不同的预测方案：7 天的 BG 读数预测接下来的 7 天或 14 天，以及 14 天的 BG 读数预测接下来的 14 天。我们的结果表明，基于 7 天观察窗口预测接下来 14 天 BG 的优化 BG 方案，在早餐后、午餐后、晚餐后、早餐前、午餐前和晚餐前预测时，其均方根误差（RMSE）的准确性分别达到了 0.958 ± 0.007、0.876 ± 0.003、0.898 ± 0.003、0.622 ± 0.003、0.814 ± 0.009 和 0.845 ± 0.005。这是第一项基于机器学习的研究，建议了一种优化的血糖监测频率，即基于血糖预测的准确性，每 7 天监测接下来的 14 天。此外，与使用连续血糖监测（CGM）读数的一小时预测模型的基准性能相比，我们基于指尖血糖测试的建议模型的准确性相当。总之，堆叠 LSTM 模型是一种很有前途的方法，可以捕捉时间序列数据中的模式，从而实现血糖水平的准确预测。使用带有常规指尖血糖采集的深度学习模型是一种很有前途、可预测且低成本的妊娠糖尿病女性血糖监测解决方案。
