Diabetes is a chronic disease caused by the inability of the pancreas to produce insulin or problems in the body to use it efficiently. It is one of the fastest growing health challenges affecting more than 400 million people worldwide, according to the World Health Organization. Intensive research is being carried out on artificial intelligence methods to help people with diabetes to optimize the way in which they use insulin, carbohydrate intakes, or physical activity. By predicting upcoming levels of blood glucose concentrations, preventive actions can be taken. Previous research studies using machine learning methods for blood glucose level predictions have mainly focused on the machine learning model used. Little attention has been given to the pre-processing of insulin and carbohydrate signals in order to mimic the human absorption processes. In this manuscript, a recurrent neural network (RNN) based model for predicting upcoming blood glucose levels in people with type 1 diabetes is combined with several carbohydrate and insulin absorption curves in order to optimize the prediction results. The proposed method is applied to data from real patients suffering type 1 diabetes mellitus (T1DM). The achieved results are encouraging, obtaining accuracy levels around 0.510 mmol/L (9.2 mg/dl) in the best scenario.

中文翻译：

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胰岛素和碳水化合物的吸收模型和深度学习以改善葡萄糖水平预测

糖尿病是一种慢性疾病，由胰腺无法产生胰岛素或身体无法有效使用胰岛素引起。据世界卫生组织称，它是影响全球 4 亿多人的增长最快的健康挑战之一。正在对人工智能方法进行深入研究，以帮助糖尿病患者优化他们使用胰岛素、碳水化合物摄入量或身体活动的方式。通过预测即将到来的血糖浓度水平，可以采取预防措施。以前使用机器学习方法进行血糖水平预测的研究主要集中在所使用的机器学习模型上。为了模拟人体吸收过程而对胰岛素和碳水化合物信号的预处理很少受到关注。在这份手稿中，一个基于循环神经网络 (RNN) 的用于预测 1 型糖尿病患者即将到来的血糖水平的模型与几条碳水化合物和胰岛素吸收曲线相结合，以优化预测结果。所提出的方法应用于来自患有 1 型糖尿病 (T1DM) 的真实患者的数据。取得的结果令人鼓舞，在最佳情况下获得约 0.510 mmol/L (9.2 mg/dl) 的准确度水平。