  Diabetes, a chronic condition that impairs how the body turns food into
energy, i.e. blood glucose, affects 38 million people in the US alone. The
standard treatment is to supplement carbohydrate intake with an artificial
pancreas, i.e. a continuous insulin pump (basal shots), as well as occasional
insulin injections (bolus shots). The goal of the treatment is to keep blood
glucose at the center of an acceptable range, as measured through a continuous
glucose meter. A secondary goal is to minimize injections, which are unpleasant
and difficult for some patients to implement. In this study, neuroevolution was
used to discover an optimal strategy for the treatment. Based on a dataset of
30 days of treatment and measurements of a single patient, a random forest was
first trained to predict future glucose levels. A neural network was then
evolved to prescribe carbohydrates, basal pumping levels, and bolus injections.
Evolution discovered a Pareto front that reduced deviation from the target and
number of injections compared to the original data, thus improving patients'
quality of life. To make the system easier to adopt, a language interface was
developed with a large language model. Thus, these technologies not only
improve patient care but also adoption in a broader population.
