Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only.
Forecasting of Glucose Levels and Hypoglycemic Events: Head-to-Head Comparison of Linear and Nonlinear Data-Driven Algorithms Based on Continuous Glucose Monitoring Data Only.
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DOI：
10.3390/s21051647
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发表时间：
2021-02-27
期刊：
影响因子：
--
通讯作者：
Facchinetti A
中科院分区：
文献类型：
--
作者：
Prendin F;Del Favero S;Vettoretti M;Sparacino G;Facchinetti A
在1型糖尿病管理中，能够准确预测未来血糖(BG)浓度和低血糖发作的算法的可用性可以使主动治疗行动成为可能，例如，消耗碳水化合物来缓解甚至避免迫在眉睫的危急事件。这种算法的唯一输入通常是连续血糖监测(CGM)传感器数据，因为其他信号(如注射的胰岛素、摄入的碳水化合物和体力活动)通常是不可用的。文献中只提出了几种基于CGM数据的预测算法，但它们是使用来自不同实验协议的数据集进行评估的，这使得比较它们的相对优点变得困难。本工作的目的是使用相同的数据集对30种不同的线性和非线性预测算法进行面对面的比较，该数据集由在10天内使用市场上最新的Dexcom G6传感器收集的124条CGM轨迹给出，并考虑了30分钟的预测期限。我们考虑了最先进的方法，特别是研究了线性黑盒方法(自回归；自回归移动平均；和自回归集成移动平均，ARIMA)和非线性机器学习方法(支持向量回归，SVR；回归随机森林；前馈神经网络，FNN；和长期短期记忆神经网络)。对于每种方法，使用总体或个性化的模型参数来评估预测准确性和低血糖检测能力。结果表明，在预测精度方面，最佳线性算法(个体化ARIMA)的预测精度与最佳非线性算法(个体化FNN)相当，均方根误差分别为22.15和21.52 mg/dL。就低血糖检测而言，最好的线性算法(个体化ARIMA)的精确度=%，召回率=82%，每天1次虚警，与最好的非线性方法(总体SVR)相当：精确度=63%，召回率=69%，虚警/天0.5。总体而言，仅在广泛的数据集上对CGM数据进行的30种算法的逐头比较表明，个性化的线性模型比总体的线性模型更有效，而使用非线性方法似乎没有明显的优势。
In type 1 diabetes management, the availability of algorithms capable of accurately forecasting future blood glucose (BG) concentrations and hypoglycemic episodes could enable proactive therapeutic actions, e.g., the consumption of carbohydrates to mitigate, or even avoid, an impending critical event. The only input of this kind of algorithm is often continuous glucose monitoring (CGM) sensor data, because other signals (such as injected insulin, ingested carbs, and physical activity) are frequently unavailable. Several predictive algorithms fed by CGM data only have been proposed in the literature, but they were assessed using datasets originated by different experimental protocols, making a comparison of their relative merits difficult. The aim of the present work was to perform a head-to-head comparison of thirty different linear and nonlinear predictive algorithms using the same dataset, given by 124 CGM traces collected over 10 days with the newest Dexcom G6 sensor available on the market and considering a 30-min prediction horizon. We considered the state-of-the art methods, investigating, in particular, linear black-box methods (autoregressive; autoregressive moving-average; and autoregressive integrated moving-average, ARIMA) and nonlinear machine-learning methods (support vector regression, SVR; regression random forest; feed-forward neural network, fNN; and long short-term memory neural network). For each method, the prediction accuracy and hypoglycemia detection capabilities were assessed using either population or individualized model parameters. As far as prediction accuracy is concerned, the results show that the best linear algorithm (individualized ARIMA) provides accuracy comparable to that of the best nonlinear algorithm (individualized fNN), with root mean square errors of 22.15 and 21.52 mg/dL, respectively. As far as hypoglycemia detection is concerned, the best linear algorithm (individualized ARIMA) provided precision = 64%, recall = 82%, and one false alarm/day, comparable to the best nonlinear technique (population SVR): precision = 63%, recall = 69%, and 0.5 false alarms/day. In general, the head-to-head comparison of the thirty algorithms fed by CGM data only made using a wide dataset shows that individualized linear models are more effective than population ones, while no significant advantages seem to emerge when employing nonlinear methodologies.
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DOI：
10.1109/jbhi.2018.2823763
发表时间：
2019-03-01
影响因子：
7.7
作者：
Gadaleta, Matteo;Facchinetti, Andrea;Rossi, Michele
通讯作者：
Rossi, Michele
影响因子：
5.4
作者：
Georga, Eleni I.;Protopappas, Vasilios C.;Fotiadis, Dimitrios I.
通讯作者：
Fotiadis, Dimitrios I.
影响因子：
16.2
作者：
Buckingham B;Chase HP;Dassau E;Cobry E;Clinton P;Gage V;Caswell K;Wilkinson J;Cameron F;Lee H;Bequette BW;Doyle FJ 3rd
通讯作者：
Doyle FJ 3rd
影响因子：
5.4
作者：
Camerlingo, Nunzio;Vettoretti, Martina;Facchinetti, Andrea
通讯作者：
Facchinetti, Andrea
影响因子：
3.9
作者：
Aliberti, Alessandro;Pupillo, Irene;Acquaviva, Andrea
通讯作者：
Acquaviva, Andrea