Designing User-Centric Behavioral Interventions to Prevent Dysglycemia with Novel Counterfactual Explanations

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.