Neural Computation, Vol 10, 941-953, Copyright © 1998 by The MIT Press
Predictive Neural Networks for Learning the Time Course of Blood Glucose Levels From the Complex Interaction of Counterregulartory Hormones
Klaus Prank, Clemens Jurgens, Alexander von zur Huhlen and Georg Brabant
Diabetes mellitus is a widespread disease associated with an impaired
hormonal regulation of normal blood glucose levels. Patients with
insulin-dependent diabetes mellitus (IDDM) who practice conventional
insulin therapy are at risk of developing hypoglycemia (low levels of blood
glucose), which can lead to severe dysfunction of the central nervous
system. In large retrospective studies, up to approximately 4%
of deaths of patients with IDDM have been attributed to hypoglycemia
(Cryer, Fisher, & Shamoon, 1994; Tunbridge, 1981; Deckert, Poulson,
& Larsen, 1978). Thus, a better understanding of the complex
hormonal interaction preventing hypoglycemia is crucial for treatment.
Experimental data from a study on insulin-induced hypoglycemia in healthy
subjects are used to demonstrate that feedforward neural networks are
capable of predicting the time course of blood glucose levels from the
complex interaction of glucose counterregulatory (glucose-raising) hormones
and insulin. By simulating the deficiency of single hormonal factors in
this regulatory network, we 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.
