Optimization of Medical Treatment Decisions for Type 2 Diabetes

The research objective of this project is to develop new mathematical models and solution methodologies to study the design of optimal treatment plans for type 2 diabetes. The research will begin by investigating models for the management of cardiovascular risk using common drug treatment options such as cholesterol and blood pressure lowering medication. Algorithmic methods will be developed for computing optimal and approximate (near optimal) treatment guidelines in the presence of uncertainty about a patient's future health status. The models will consider multiple perspectives including a patient's quality adjusted lifespan, the costs of treatment, and the cost of diabetes related complications to the health system.

According to the American Diabetes Association, there are more than 20 million children and adults in the United States who have diabetes. Of the affected population, approximately 90 percent have type 2 diabetes. Currently, several risk models exist to predict the probability of complications related to type 2 diabetes, including cardiovascular complications such as heart attack and stroke; however, there has been limited investigation of how to use these models to make optimal treatment decisions. This project seeks to bridge this gap by furthering the basic knowledge of how to optimally treat type 2 diabetes over the course of a patient's lifetime. It is anticipated that discoveries from this research project will be transferrable to the treatment of other diseases.