Management insulin dosing for diabetes using a partially observable Markov decision process with missing data imputation (opens in new tab)

Missing data in continuous glucose monitoring (CGM) poses a significant challenge for applying sequential decision-making models to diabetes management. This study evaluates how missing-data imputation affects downstream Partially Observable Markov Decision Process (POMDP)-based policy outputs using real CGM trajectories from the Stanford Continuous Glucose Monitoring Database. Three imputation methods are compared: mean imputation, linear interpolation, and a bridge-based adjusted Metropolis...