Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:4426-4429. doi: 10.1109/EMBC48229.2022.9870983.
Closed-loop diabetes management has been shown to indicate improved glycaemic control and better compliance over open loop diabetes management. Currently, commercially available diabetes management devices rely on continuous glucose monitoring (CGM) sensors which monitor glucose levels from the interstitial fluid (ISF). As there exists a physiological delay between the blood glucose levels compared to the ISF glucose levels, it is crucial to predict or forecast glucose levels, in order to prevent hyperglycaemic events due to delayed insulin dosing. Accuracy of the forecast also needs to be optimum such that overdosing on insulin does not lead to hypoglycaemia. In this paper, we describe a novel Long Short Term Memory (LSTM) network which follows a wide and deep approach for different features to deliver an accurate glucose prediction output. It achieved a Mean Absolute Relative Difference (MARD) of 2.61 and Root Mean Squared Error (MSE) of 5.04. Clinical relevance- This work is relevant for closed-loop diabetes management devices, which are currently being used to manage Type 1 Diabetes (T1D).
闭环糖尿病管理已被证明在改善血糖控制和提高依从性方面优于开环糖尿病管理。目前，市售的糖尿病管理设备依赖于连续血糖监测（CGM）传感器，这些传感器监测间质液（ISF）中的血糖水平。由于与 ISF 血糖水平相比，血糖水平存在生理延迟，因此预测或预测血糖水平至关重要，以防止由于胰岛素给药延迟而导致高血糖事件。预测的准确性也需要最佳，以避免胰岛素过量导致低血糖。在本文中，我们描述了一种新颖的长短期记忆（LSTM）网络，该网络采用广泛而深入的方法来处理不同的特征，以提供准确的血糖预测输出。它实现了 2.61 的平均绝对相对差异（MARD）和 5.04 的均方根误差（MSE）。临床相关性- 这项工作与闭环糖尿病管理设备相关，这些设备目前用于管理 1 型糖尿病（T1D）。
