Fig. 3: Performance of AI treatment model in the insulin dosage prediction.
From: Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial
a,b, Performance of daily treatment dosage prediction on the internal test set (a) (n = 42,037 insulin data points) and the external test set (b) (n = 32,484 insulin data points). Each predicted value was subsequently unrolled recurrently for K steps from the last timestep of the previous day (K = 7 for 1 d ahead of time). The error bars represent the 95% CIs. We aggregated the individual-level predictions to obtain population-level results. c,d, Comparison of actual treatment regimens and model-based treatment roll-outs of two individual patients from the internal test set (c) and the external test set (d). The blue curve is measured patient glucose values, and the orange curve is predicted glucose values given by the AI model. e,f, Association analysis of the patient outcome (for example, WTR) versus the dosage difference in treatment actions between the AI policy and the clinician policy for the internal test set (e) and the external test set (f). The dose excess, referring to the difference between the given and the AI model, suggested dose summed over per day for all patients. The shaded area represents the 95% CI. R2, coefficient of determination. MAPE, mean absolute percentage error.
