Data-driven strategies for robust forecast of continuous glucose monitoring time-series

2017 ◽  
Author(s):  
Samuele Fiorini ◽  
Chiara Martini ◽  
Davide Malpassi ◽  
Renzo Cordera ◽  
Davide Maggi ◽  
...  
Keyword(s):  
Time Series ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Data Driven ◽  
Monitoring Time

Continuous Glucose Monitoring Time Series and Hypo/Hyperglycemia Prevention: Requirements, Methods, Open Problems

2008 ◽  
Vol 4 (3) ◽  
pp. 181-192 ◽  
Author(s):  
Giovanni Sparacino ◽  
Andrea Facchinetti ◽  
Alberto Maran ◽  
Claudio Cobelli
Keyword(s):  
Time Series ◽  
Continuous Glucose Monitoring ◽  
Glucose Monitoring ◽  
Open Problems ◽  
Monitoring Time

Time-Series Analysis of Continuous Glucose Monitoring Data to Predict Treatment Efficacy in Patients with T2DM

2021 ◽  
Author(s):  
Li Li ◽  
Jie Sun ◽  
Liemin Ruan ◽  
Qifa Song
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Continuous Glucose Monitoring ◽  
Treatment Efficacy ◽  
Treatment Effects ◽  
Glucose Monitoring ◽  
Therapeutic Effects ◽  
Time Series Clustering ◽  
Glucose Lowering ◽  
Series Analysis

Abstract Context There is a challenge to predict treatment effects in patients with T2DM. Objective To assess and predict treatment effects in patients with T2DM through time-series analysis of continuous glucose monitoring (CGM) measurements. Design We extracted and clustered the trend components of CGM measurements to generate representative time-series profiles, which were used as a predictor of treatment effects in groups of patients. Setting and Participants We recruited 111 outpatients with T2DM at Ningbo City First Hospital. Intervention The patients underwent CGM measurement for 14 days at the beginning of glucose-lowering treatment. Main Outcome Measures HbA1c and FPG were obtained at the beginning and 6-month of treatment. Results 111 patients each had 960 –1344 CGM measurements for 14 days at 96 measurements per day. The patients were classified into three groups according to the profiles of trend components of CGM observed values by time-series clustering method, including decreasing (47 patients), increasing (26 patients), and unchanged (38 patients) profiles. After six-month glucose-lowering treatment, FPG declined from 10.2 to 6.8 mmol/L (a decline of 3.5 mmol/L) in the decreasing group, from 8.9 to 9.2 mmol/L (a rise of 0.3 mmol/L) in the increasing group, and from 8.4 to 7.5 mmol/L (a decline of 0.9 mmol/L). The changes of HbA1c were 2.2%, 0.2%, and 0.9% for the three groups (P<0.01), respectively. Conclusions Clustering of the trend components of CGM data generates representative CGM profiles that are predictive of six-month therapeutic effects for T2DM.

scholarly journals CGMTSA: An R package for continuous glucose monitoring time series data analysis

2020 ◽  
Author(s):  
Jian Shao ◽  
Tao Xu ◽  
Kaixin Zhou
Keyword(s):  
Time Series ◽  
Continuous Glucose Monitoring ◽  
Time Series Data ◽  
Glucose Monitoring ◽  
R Package ◽  
Series Data ◽  
Missing Data Imputation ◽  
Outlier Identification ◽  
Time Series Data Analysis ◽  
Processing Steps

AbstractThe R package CGMTSA was developed to facilitate investigations that examine the continuous glucose monitoring (CGM) data as a time series. Accordingly, novel time series functions were introduced to: 1) enable more accurate missing data imputation and outlier identification; 2) calculate recommended CGM metrics as well as key time series parameters; 3) plot interactive and 3D graphs that allow direct visualizations of temporal CGM data and time series model optimization. The software was designed to accommodate all popular CGM devices and support all common data processing steps.

Glucose Concentration can be Predicted Ahead in Time From Continuous Glucose Monitoring Sensor Time-Series

2007 ◽  
Vol 54 (5) ◽  
pp. 931-937 ◽  
Author(s):  
G. Sparacino ◽  
F. Zanderigo ◽  
S. Corazza ◽  
A. Maran ◽  
A. Facchinetti ◽  
...  
Keyword(s):  
Time Series ◽  
Continuous Glucose Monitoring ◽  
Glucose Concentration ◽  
Glucose Monitoring
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