论文标题
基于连续葡萄糖监测数据的短期血糖预测
Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data
论文作者
论文摘要
连续的葡萄糖监测（CGM）为糖尿病管理带来了重要的机会。这项研究探讨了CGM数据作为数字决策支持工具的输入。我们研究了如何将复发性神经网络（RNN）用于短期血糖（STBG）预测，并将RNN与使用自回归的集成运动平均值（ARIMA）进行比较与常规的时间序列预测。考虑到未来长达90分钟的预测范围。在这种情况下，我们评估了基于人群和特定于患者的RNN，并将其与患者特异性的Arima模型进行对比，并进行了简单的基线，预测了最后观察到的未来观察结果。我们发现，基于人群的RNN模型是在考虑到患者特定数据的情况下，是在考虑的预测范围内的最佳性能模型。这证明了RNN在糖尿病患者中预测STBG预测的潜力在检测/减轻STBG中的严重事件，特别是降血糖事件中的潜力。但是，需要进一步的研究，就研究的STBG预测模型的鲁棒性和实际使用。
Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction and compare the RNNs to conventional time-series forecasting using Autoregressive Integrated Moving Average (ARIMA). A prediction horizon up to 90 min into the future is considered. In this context, we evaluate both population-based and patient-specific RNNs and contrast them to patient-specific ARIMA models and a simple baseline predicting future observations as the last observed. We find that the population-based RNN model is the best performing model across the considered prediction horizons without the need of patient-specific data. This demonstrates the potential of RNNs for STBG prediction in diabetes patients towards detecting/mitigating severe events in the STBG, in particular hypoglycemic events. However, further studies are needed in regards to the robustness and practical use of the investigated STBG prediction models.
