# Continuous Glucose Forecasting

Glucose digital twins in septic intensive care previously refit the model only after forecast error crossed a threshold, a reactive correction. This work reports a proactive workflow: a frozen PatchTST foundation model produces a one-step forecast, and an Actor–Critic reinforcement-learning agent uses the last 30-minute glucose window and recent residual history to apply a correction bounded at ±0.05 to that first step, which is then rolled out recursively over 15 steps. On minute-by-minute continuous glucose monitoring from 10 septic ICU patients, the pretrained agent lowered one-step forecast error in all 10 without patient-specific retraining, and mean absolute percentage error on the source patient's test set fell from 0.49% to 0.34%.

Glucose digital twins in septic intensive care previously refit the model only after forecast error crossed a threshold, a reactive correction. This work reports a proactive workflow: a frozen PatchTST foundation model produces a one-step forecast, and an Actor–Critic reinforcement-learning agent uses the last 30-minute glucose window and recent residual history to apply a correction bounded at ±0.05 to that first step, which is then rolled out recursively over 15 steps. On minute-by-minute continuous glucose monitoring from 10 septic ICU patients, the pretrained agent lowered one-step forecast error in all 10 without patient-specific retraining, and mean absolute percentage error on the source patient's test set fell from 0.49% to 0.34%.