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Full-Text Articles in Biomedical Engineering and Bioengineering

Hypoglycemia Early Alarm Systems Based On Multivariable Models, Kamuran Turksoy, Elif S. Bayrak, Lauretta Quinn, Elizabeth Littlejohn, Derrick K. Rollins Sr., Ali Cinar Feb 2015

Hypoglycemia Early Alarm Systems Based On Multivariable Models, Kamuran Turksoy, Elif S. Bayrak, Lauretta Quinn, Elizabeth Littlejohn, Derrick K. Rollins Sr., Ali Cinar

Derrick K Rollins, Sr.

Hypoglycemia is a major challenge of artificial pancreas systems and a source of concern for potential users and parents of young children with Type 1 diabetes (T1D). Early alarms to warn of the potential of hypoglycemia are essential and should provide enough time to take action to avoid hypoglycemia. Many alarm systems proposed in the literature are based on interpretation of recent trends in glucose values. In the present study, subject-specific recursive linear time series models are introduced as a better alternative to capture glucose variations and predict future blood glucose concentrations. These models are then used in hypoglycemia early ...


Multiple-Input Subject-Specific Modeling Of Plasma Glucose Concentration For Feedforward Control, Kaylee Renee Kotz, Ali Cinar, Yong Mei, Amy Roggendorf, Elizabeth Littlejohn, Laurie Quinn, Derrick K. Rollins Sr. Jan 2014

Multiple-Input Subject-Specific Modeling Of Plasma Glucose Concentration For Feedforward Control, Kaylee Renee Kotz, Ali Cinar, Yong Mei, Amy Roggendorf, Elizabeth Littlejohn, Laurie Quinn, Derrick K. Rollins Sr.

Chemical and Biological Engineering Publications

The ability to accurately develop subject-specific, input causation models, for blood glucose concentration (BGC) for large input sets can have a significant impact on tightening control for insulin dependent diabetes. More specifically, for Type 1 diabetics (T1Ds), it can lead to an effective artificial pancreas (i.e., an automatic control system that delivers exogenous insulin) under extreme changes in critical disturbances. These disturbances include food consumption, activity variations, and physiological stress changes. Thus, this paper presents a free-living, outpatient, multiple-input, modeling method for BGC with strong causation attributes that is stable and guards against overfitting to provide an e ffective ...


Hypoglycemia Early Alarm Systems Based On Multivariable Models, Kamuran Turksoy, Elif S. Bayrak, Lauretta Quinn, Elizabeth Littlejohn, Derrick K. Rollins Sr., Ali Cinar Jan 2013

Hypoglycemia Early Alarm Systems Based On Multivariable Models, Kamuran Turksoy, Elif S. Bayrak, Lauretta Quinn, Elizabeth Littlejohn, Derrick K. Rollins Sr., Ali Cinar

Chemical and Biological Engineering Publications

Hypoglycemia is a major challenge of artificial pancreas systems and a source of concern for potential users and parents of young children with Type 1 diabetes (T1D). Early alarms to warn of the potential of hypoglycemia are essential and should provide enough time to take action to avoid hypoglycemia. Many alarm systems proposed in the literature are based on interpretation of recent trends in glucose values. In the present study, subject-specific recursive linear time series models are introduced as a better alternative to capture glucose variations and predict future blood glucose concentrations. These models are then used in hypoglycemia early ...


An Algorithm For Optimally Fitting A Wiener Model, Lucas P. Beverlin, Derrick K. Rollins Sr., Nisarg Vyas, David Andre Jan 2011

An Algorithm For Optimally Fitting A Wiener Model, Lucas P. Beverlin, Derrick K. Rollins Sr., Nisarg Vyas, David Andre

Chemical and Biological Engineering Publications

The purpose of this work is to present a new methodology for fitting Wiener networks to datasets with a large number of variables. Wiener networks have the ability to model a wide range of data types, and their structures can yield parameters with phenomenological meaning. There are several challenges to fitting such a model: model stiffness, the nonlinear nature of a Wiener network, possible overfitting, and the large number of parameters inherent with large input sets. This work describes a methodology to overcome these challenges by using several iterative algorithms under supervised learning and fitting subsets of the parameters at ...


An Extended Data Mining Method For Identifying Differentially Expressed Assay-Specific Signatures In Functional Genomic Studies, Derrick K. Rollins Sr., Ai-Ling Teh Jan 2010

An Extended Data Mining Method For Identifying Differentially Expressed Assay-Specific Signatures In Functional Genomic Studies, Derrick K. Rollins Sr., Ai-Ling Teh

Chemical and Biological Engineering Publications

Background: Microarray data sets provide relative expression levels for thousands of genes for a small number, in comparison, of different experimental conditions called assays. Data mining techniques are used to extract specific information of genes as they relate to the assays. The multivariate statistical technique of principal component analysis (PCA) has proven useful in providing effective data mining methods. This article extends the PCA approach of Rollins et al. to the development of ranking genes of microarray data sets that express most differently between two biologically different grouping of assays. This method is evaluated on real and simulated data and ...