Corpus ID: 434860

Towards Neural Network Model for Insulin/Glucose in Diabetics-II

@article{Zitar2005TowardsNN,
  title={Towards Neural Network Model for Insulin/Glucose in Diabetics-II},
  author={R. A. Zitar and Abdulkareem Al-Jabali},
  journal={Informatica (Slovenia)},
  year={2005},
  volume={29},
  pages={227-232}
}
In this work we extending our investigations for a general neural network model that resembles the interactions between glucose concentration levels and amount of insulin injected in the bodies of diabetics. We use real data for 70 different patients of diabetics and build on it our model. Two types of neural networks (NN’s) are experimented in building that model; the first type is called the LevenbergMarquardt (LM) training algorithm of multilayer feed forward neural network (NN), the other… Expand
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References

SHOWING 1-10 OF 31 REFERENCES
Predictive Neural Networks for Learning the Time Course of Blood Glucose Levels from the Complex Interaction of Counterregulatory Hormones
TLDR
Simulating the deficiency of single hormonal factors in this regulatory network found that the predictive impact of glucagon, epinephrine, and growth hormone secretion, but not of cortisol and norepinephrine, were dominant in restoring normal levels of blood glucose following hypoglycemia. Expand
Retrospective validation of a physiological model of glucose-insulin interaction in type 1 diabetes mellitus.
TLDR
It is found that the physiological model could only be parameterized for data from 24 (80%) of the 30 patients in the study, and comparison of observed and predicted blood glucose data from these 24 patients over a period of 5-6 days following parameter estimation revealed a mean (+/- SD) root mean square deviation between measured and simulated blood glucose values. Expand
Comparison of parametrized models for computer-based estimation of diabetic patient glucose response.
TLDR
It is demonstrated that, given four daily blood glucose measurements and two daily insulin injections, a parametrized model of patientBlood glucose response to insulin can provide relevant data in the estimation of a patient's future blood glucose response in terms of past blood glucose measurement and insulin injections. Expand
A probabilistic approach to glucose prediction and insulin dose adjustment: description of metabolic model and pilot evaluation study.
TLDR
A model of carbohydrate metabolism has been implemented as a causal probabilistic network, allowing explicit representation of the uncertainties involved in the prediction of 24-h blood glucose profiles in insulin-dependent diabetic subjects, and in several cases seemed more appropriate than the doses actually administered to the patients. Expand
Quantitative estimation of insulin sensitivity.
TLDR
From a single glucose injection it is possible to obtain a quantitative index of insulin sensitivity that may have clinical applicability, and this index was defined as the ratio of two parameters of the chosen model and could be estimated with good reproducibility from the 300 mg/kg injection experiments. Expand
Computer model for mechanisms underlying ultradian oscillations of insulin and glucose.
TLDR
A parsimonious mathematical model including the major mechanisms involved in glucose regulation was developed and mimicked all experimental findings so far observed for these ultradian oscillations, including self-sustained oscillations during constant glucose infusion. Expand
Fundamentals of Artificial Neural Networks
  • M. Hassoun
  • Computer Science
  • Proceedings of the IEEE
  • 1996
TLDR
Fundamentals of Artificial Neural Networks provides the first systematic account of artificial neural network paradigms by identifying clearly the fundamental concepts and major methodologies underlying most of the current theory and practice employed by neural network researchers. Expand
Effect of insulin on the distribution and disposition of glucose in man.
TLDR
It is concluded that hyperinsulinemia, independent of hyperglycemia, markedly increases the exchangeable mass of glucose in the body, presumably reflecting the accumulation of free, intracellular glucose in insulin-dependent tissues. Expand
Neural Networks in Signal Processing
TLDR
The fundamentals of Neural Networks in signal processing and their applications in tasks such as recognition/identification and control are reviewed and topics covered include dynamic modeling, model based ANN’s, statistical learning, eigen structure based processing and generalization structures. Expand
Is blood glucose predictable from previous values? A solicitation for data.
An important question about blood glucose control in diabetes is, Can present and future blood glucose values be predicted from recent blood glucose history? If this is possible, new continuous bloodExpand
...
1
2
3
4
...