Real-Time Statistical Modeling of Blood Sugar
Real-Time Statistical Modeling of Blood Sugar
复制标题
血糖实时统计模型
DOI：
10.1007/s10916-015-0301-8
复制
发表时间：
2015
影响因子：
5.3
通讯作者：
José Bravo
中科院分区：
文献类型：
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作者：
Mwaffaq Otoom;H. Alshraideh;Hisham M. Almasaeid;D. López;José Bravo
糖尿病被认为是一种慢性疾病，给世界带来各种各样的损失。控制糖尿病的一个主要挑战是正确胰岛素剂量的真实的时间确定。在本文中，我们开发了一个原型的真实的时间血糖控制，与云集成。我们的系统通过观察血糖水平并根据患者的历史数据相应地确定适当的胰岛素剂量来控制血糖，所有这些都是真实的实时和自动的。为了确定合适的胰岛素剂量，我们提出了两种统计模型来建模血糖曲线，即ARIMA和马尔可夫模型。我们的实验用于评估两个模型的性能表明，ARIMA模型优于马尔可夫模型的预测精度。
Diabetes is considered a chronic disease that incurs various types of cost to the world. One major challenge in the control of Diabetes is the real time determination of the proper insulin dose. In this paper, we develop a prototype for real time blood sugar control, integrated with the cloud. Our system controls blood sugar by observing the blood sugar level and accordingly determining the appropriate insulin dose based on patient’s historical data, all in real time and automatically. To determine the appropriate insulin dose, we propose two statistical models for modeling blood sugar profiles, namely ARIMA and Markov-based model. Our experiment used to evaluate the performance of the two models shows that the ARIMA model outperforms the Markov-based model in terms of prediction accuracy.