Predicting Glucose Levels in Patients with Type1 Diabetes Based on Physiological and Activity Data
Predicting Glucose Levels in Patients with Type1 Diabetes Based on Physiological and Activity Data
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根据生理和活动数据预测 1 型糖尿病患者的血糖水平
DOI：
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发表时间：
2018
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影响因子：
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通讯作者：
Boyi Jiang
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文献类型：
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作者：
M. Vahedi;Koenrad B. MacBride;Woo Wunsik;Yosep Kim;C. Fong;Andrew J. Padilla;M. Pourhomayoun;A. Zhong;Sameer R Kulkarni;S. Arunachalam;Boyi Jiang
管理1型糖尿病患者的血糖水平是更好地控制血糖的绝对必要条件。在本文中，我们提出了一种预测模型，该模型使用生理测量和身体活动来预测连续葡萄糖水平，并帮助患者减少和预防高血糖和低血糖暴露，这些情况对患者健康有害。本研究的数据包括使用Medtronic MiniMed™ 530 G胰岛素输送系统（带Enlite™探头）从93名糖尿病患者中收集的4个月的生理测量、体力活动和营养信息。经过数据预处理、缺失值填补、特征提取和特征选择，得到一组180个特征来表示原始数据。然后，基于机器学习算法开发适当的预测模型来预测连续葡萄糖水平。计算了系统的预测精度和预测误差，对系统的性能进行了评价。结果表明，预测葡萄糖水平与皮下葡萄糖探头测量的实际探头葡萄糖（SG）值密切相关。
Managing blood glucose levels for type 1 diabetes patients is an absolute necessity to better glycemic control. In this paper, we present a predictive model that uses physiological measurements and physical activity to predict continuous glucose levels and help patients reduce and prevent hyperglycemia and hypoglycemia exposure, conditions that are harmful to patient health. The data of this research includes 4 months of physiological measurements, physical activity, and nutrition information collected from 93 patients with diabetes using the Medtronic MiniMed™ 530G insulin delivery system with Enlite™ sensor. After data preprocessing, missing value imputation, feature extraction, and feature selection, a set of 180 features were derived to represent the raw data. Then, an appropriate predictive model was developed based on machine-learning algorithms to predict continuous glucose levels. The prediction accuracy and error have been calculated to evaluate the performance of the system. The results demonstrated that the predicted glucose levels closely followed the actual sensor glucose (SG) values measured by subcutaneous glucose sensor.