Blood Glucose Level Prediction using Physiological Models and Support Vector Regression

Date: 

Fri Apr 18, 2014

Host: 

Razvan Bunescu
Ohio University, School of Electrical Engineering and Computer Science

Category: 

Data Modeling and Integration

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ABSTRACT

Patients with diabetes must continually monitor their blood glucose levels and adjust insulin doses, striving to keep blood glucose levels as close to normal as possible. Blood glucose levels that deviate from the normal range can lead to serious short-term and long-term complications. An automatic prediction model that warned people of imminent changes in their blood glucose levels would enable them to take preventive action. Modeling inter-patient differences and the combined effects of insulin and life events on blood glucose have been particularly challenging in the design of accurate blood glucose forecasting systems. In this paper, we describe a solution that uses a generic physiological model of blood glucose dynamics to generate informative features for a Support Vector Regression model that is trained on patient specific data. Experimental results show that the new prediction model outperforms all three diabetes experts involved in the study, thus demonstrating the utility of using the generic physiological features in machine learning models that are individually trained for every patient.
 

SPEAKER BIOGRAPHY

Razvan Bunescu is an Associate Professor of Computer Science at Ohio University. His research interests include machine learning, computational linguistics, and biomedical informatics. He graduated from the University of Texas at Austin in 2007 with a PhD thesis on machine learning methods for information extraction. Since then, a major focus of his research has been on exploiting large scale weakly structured collections of documents for natural language processing applications. More recently, in collaboration with a group of diabetes experts, he has been working on machine learning models for medical informatics tasks such as blood glucose level prediction and glycemic variability detection. His research has been funded by grants from the National Science Foundation and Ohio University.
 

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