Departmental Papers (CIS)

Date of this Version


Document Type

Conference Paper


The IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015), Dallas, USA, October 21-23, 2015


About 30%-40% of Type 1 Diabetes (T1D) patients in the United States use insulin pumps. Current insulin infusion systems require users to manually input meal carb count and approve or modify the system-suggested meal insulin dose. Users can give correction insulin boluses at any time. Since meal carbohydrates and insulin are the two main driving forces of the glucose physiology, the user-specific eating and pump-using behavior has a great impact on the quality of glycemic control.

In this paper, we propose an “Eat, Trust, and Correct” (ETC) framework to model the T1D insulin pump users’ behavior. We use machine learning techniques to analyze the user behavior from a clinical dataset that we collected on 55 T1D patients who use insulin pumps. We demonstrate the usefulness of the ETC behavior modeling framework by performing in silico experiments. To this end, we integrate the user behavior model with an individually parameterized glucose physiological model, and perform probabilistic model checking on the user-in-the-loop system. The experimental results show that switching behavior types can significantly improve a patient’s glycemic control outcomes. These analysis results can boost the effectiveness of T1D patient education and peer support.

Subject Area

CPS Medical

Publication Source

The IEEE International Conference on Healthcare Informatics 2015 (ICHI 2015)

Copyright/Permission Statement

© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.


type 1 diabetes, patient behavior, data-driven modeling and analysis, data-driven verification, blood glucose control, insulin pump, closed-loop verification, probabilistic model checking, physiological modeling, medical cyber-physical systems



Date Posted: 14 October 2015

This document has been peer reviewed.