Bayesian analysis of blood glucose time series from diabetes home monitoring

This paper describes the application of a novel Bayesian estimation techniq ue to extract the structural components, i.e., trend and daily patterns, fr om blood glucose level time series coming from home monitoring of insulin d ependent diabetes mellitus patients. The problem is formulated through a se t of stochastic equations, and is solved in a Bayesian framework by using a Markov chain Monte Carlo technique. The potential of the method is illustr ated by analyzing data coming from the home monitoring of a 14-year old mal e patient.