Gaussian Process Regression for Modelling Blood Glucose Dynamics
Typ
Examensarbete för masterexamen
Program
Engineering mathematics and computational science (MPENM), MSc
Publicerad
2021
Författare
Waters, Noel
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Abstract
Type 2 diabetes is a disease characterized by poor control of blood glucose levels.
Continuous Glucose Monitoring (CGM) is an increasingly popular technology for
studying glucose levels and evaluating treatment effects. Although CGM technology
gives potential for granular insights into disease characteristics, more can be done
in terms of exploiting this rich and dense data source to the fullest. This study
aims to investigate the usefulness of Gaussian process regression as a framework for
modelling blood glucose dynamics. The CGM data were collected from a previous
clinical trial on a cohort of overweight and obese type 2 diabetes patients. Gaussian
process modelling tools were used to capture short-term and recurring trends while
adjusting for long-term changes in glucose control. Results indicate that structure
such as periodicity can be successfully modelled. Interpreting specific modelling
results showed to be challenging due to a high degree of uncertainty in the model
hyperparameters. Non-stationary models should be considered to better account
for the irregular occurrence of meal-related glucose spikes and differences between
day and night glycemic variability. Finally, the periodic properties of blood glucose
dynamics should be further explored.
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Beskrivning
Ämne/nyckelord
Gaussian Processes, Continuous Glucose Monitoring, Periodograms, Type 2 Diabetes