  Maintaining normal blood glucose levels through lifestyle behaviors is
central to maintaining health and preventing disease. Frequent exposure to
dysglycemia (i.e., abnormal glucose events such as hyperlycemia and
hypoglycemia) leads to chronic complications including diabetes, kidney disease
and need for dialysis, myocardial infarction, stroke, amputation, and death.
Therefore, a tool capable of predicting dysglycemia and offering users
actionable feedback about how to make changes in their diet, exercise, and
medication to prevent abnormal glycemic events could have significant societal
impacts. Counterfactual explanations can provide insights into why a model made
a particular prediction by generating hypothetical instances that are similar
to the original input but lead to a different prediction outcome. Therefore,
counterfactuals can be viewed as a means to design AI-driven health
interventions to prevent adverse health outcomes such as dysglycemia. In this
paper, we design GlyCoach, a framework for generating counterfactual
explanations for glucose control. Leveraging insights from adversarial
learning, GlyCoach characterizes the decision boundary for high-dimensional
health data and performs a grid search to generate actionable interventions.
GlyCoach is unique in integrating prior knowledge about user preferences of
plausible explanations into the process of counterfactual generation. We
evaluate GlyCoach extensively using two real-world datasets and external
simulators from prior studies that predict glucose response. GlyCoach achieves
87\% sensitivity in the simulation-aided validation, surpassing the
state-of-the-art techniques for generating counterfactual explanations by at
least $10\%$. Besides, counterfactuals from GlyCoach exhibit a $32\%$ improved
normalized distance compared to previous research.
