Personalized Diabetes Management
Recommending personalized treatments for diabetes patients
Diabetes management is still largely one-size-fits-all
This problem is further complicated by the plethora of treatment options, which can be any combination of oral drugs, metformin, and insulin. Current approaches in prescriptive analytics typically consider each treatment option independently, which is not a realistic assumption in this setting due to treatment interaction effects.
We recommend treatments that are interpretable and medically sensible
With observational data from a large Electronic Medical Records database with over 48,000 patients with type 2 diabetes, we used Optimal Policy Trees to learn an interpretable and useful treatment recommendation policy based on patients' HbA1c levels, years since diagnosis, and number of visits. For example, patients with low HbA1c are prescribed metformin alone, which is one of the least intense treatment options, a finding that is consistent with the understanding of the medical community.
Illustrative Optimal Policy Tree prescribing treatments
Clinically meaningful reduction of HbA1c
Why is the Interpretable AI solution unique?
A novel approach to policy learning
Optimal Policy Trees are the first scalable and optimal solution to this class of problem
Work directly with numeric treatments
This framework leverages multiple treatments with doses directly without losing information by discretizing them