Personalized diabetes management

Abstract

In this thesis, we present a system to make personalized lifestyle and health decisions for diabetes management, as well as for general health and diet management. In particular, we address the following components of the system: (a) eciently learning preferences through a dynamic questionnaire that accounts for human behavior; (b) modeling blood glucose behavior and updating these models to match individual measurements; and (c) using the learned preferences and blood glucose models to generate an overall diet and exercise plan using mixed-integer robust optimization. In the first part, we propose a method to address (a) above, using integer and robust optimization. Despite the importance of personalization for successful lifestyle modification, current systems for diabetes and dieting do not attempt to use individual preferences to make suggestions. We present a general approach to learning preferences, that includes an efficient and dynamic questionnaire that accounts for response errors, and robust optimization models using risk measures to account for the commonly seen human behavior of loss aversion. We then address part (b) of our system, by first modeling blood glucose behavior as a function of food consumed and exercise performed. We rely on known attributes of dierent foods as well as individual data to build these models. We also show how we use optimization to dynamically update the parameters of the model using new data as it becomes available. In the third part of this thesis, we address (c) by using mixed-integer optimization to nd an optimal meal and exercise plan for the user that minimizes blood glucose levels while maximizing preferences. We then present a robust counterpart to the formulation, that minimizes blood glucose levels subject to uncertainty in the blood glucose models. We have implemented our system as an online application, and conclude by showing a demonstration of the overall program.