Presenters

Sarah PetersFollow

Academic department under which the project should be listed

SPCEET - Electrical and Computer Engineering

Faculty Sponsor Name

Bill Diong

Abstract (300 words maximum)

Gestational diabetes is one of the most common issues that a pregnant woman encounters that could result in harm to both the woman and child. Due to this issue, the woman’s glucose and insulin levels should be carefully monitored throughout her pregnancy to assess the need for prescribed diabetic medication to help regulate those levels. In this research, the objectives is to develop a MATLAB computer program of the Oral Minimal Model, which is a model that can be used to estimate a person’s insulin sensitivity from an oral glucose tolerance test (OGTT) where plasma glucose and insulin levels are determined from blood samples collected from subjects at seven different time instants of 0, 10, 20, 30, 60, 90, and 120 minutes. As of this moment, the program has been completed, tested, and then applied to patient data collected by Dr. Katie Ingram’s research team, producing some promising results on estimating their individual glucose effectiveness (Sg), insulin sensitivity (Si), and rate constant (k3) characteristics.

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Oral Minimal Model for Gestational Diabetes

Gestational diabetes is one of the most common issues that a pregnant woman encounters that could result in harm to both the woman and child. Due to this issue, the woman’s glucose and insulin levels should be carefully monitored throughout her pregnancy to assess the need for prescribed diabetic medication to help regulate those levels. In this research, the objectives is to develop a MATLAB computer program of the Oral Minimal Model, which is a model that can be used to estimate a person’s insulin sensitivity from an oral glucose tolerance test (OGTT) where plasma glucose and insulin levels are determined from blood samples collected from subjects at seven different time instants of 0, 10, 20, 30, 60, 90, and 120 minutes. As of this moment, the program has been completed, tested, and then applied to patient data collected by Dr. Katie Ingram’s research team, producing some promising results on estimating their individual glucose effectiveness (Sg), insulin sensitivity (Si), and rate constant (k3) characteristics.