IEEE Trans Biomed Eng. 2022 Feb;69(2):654-665. doi: 10.1109/TBME.2021.3103127. Epub 2022 Jan 20.
According to the World Health Organization, about 422 million people worldwide have type 1 or type 2 diabetes (T1D, T2D), with the latter accounting for 90-95% of cases. Safe and effective treatment of patients with diabetes requires accurate and frequent monitoring of their blood sugar levels. Continuous glucose monitoring (CGM) is a monitoring technology developed to address this need, and its use among U.S. T1D patients has increased from 6% in 2011 to 38% in 2018 and continues to increase worldwide in both T1D and T2D. This paper presents a data-driven approach to determine Ω, a finite set of representative daily profiles (motifs) such that almost any daily CGM profile generated by a patient can be matched to one of the motifs in Ω. The training data set (9741 profiles) was used to identify 8 candidate sets of motifs, while the validation data set (14 175 profiles) was used to select the final set Ω. The robustness of Ω was established by using it to successfully classify (match against a representative daily profile in Ω) 99.0% of 42 595 daily CGM profiles in the testing data set. All data sets contained daily CGM profiles from six studies involving T1D and T2D patients using a variety of treatment modes, including daily insulin injections, insulin pumps, or artificial pancreas (AP). The classified profiles can be used in predictive modeling, decision support, and automated control systems (e.g., AP).
根据世界卫生组织的数据，全球约有 4.22 亿人患有 1 型或 2 型糖尿病（T1D、T2D），其中后者占 90-95%。安全有效地治疗糖尿病患者需要准确且频繁地监测其血糖水平。连续血糖监测（CGM）是为满足这一需求而开发的监测技术，其在美国 T1D 患者中的使用比例从 2011 年的 6%增加到 2018 年的 38%，并在 T1D 和 T2D 患者中在全球范围内继续增加。本文提出了一种数据驱动的方法来确定 Ω，这是一个有限的代表性日常轮廓（基序）集合，使得几乎任何由患者生成的日常 CGM 轮廓都可以与 Ω 中的一个基序匹配。训练数据集（9741 个轮廓）用于识别 8 个候选基序集，而验证数据集（14175 个轮廓）用于选择最终的基序集 Ω。通过使用它成功地对 42595 个日常 CGM 轮廓中的 99.0%进行分类（与 Ω 中的代表性日常轮廓匹配），从而确定了 Ω 的稳健性。所有数据集均包含来自六个研究的日常 CGM 轮廓，这些研究涉及 T1D 和 T2D 患者，使用多种治疗模式，包括每日胰岛素注射、胰岛素泵或人工胰腺（AP）。分类后的轮廓可用于预测建模、决策支持和自动化控制系统（例如 AP）。
