Predictive Neural Networks for Learning the Time Course of Blood Glucose Levels from the Complex Interaction of Counterregulatory Hormones

• Published in: Neural computation Vol. 10; no. 4; pp. 941 - 953
• Main Authors: Prank, Klaus, Jürgens, Clemens, von zur Mühlen, Alexander, Brabant, Georg
• Format: Journal Article
• Language: English
• Published: One Rogers Street, Cambridge, MA 02142-1209, USA MIT Press 15.05.1998
• Subjects: Applied sciences; Artificial intelligence; Biological and medical sciences; Blood Glucose - metabolism; Computer science; control theory; systems; Computerized, statistical medical data processing and models in biomedicine; Epinephrine - secretion; Exact sciences and technology; Glucagon - secretion; Hormones - secretion; Human Growth Hormone - secretion; Humans; Hypoglycemic Agents - pharmacology; Insulin - pharmacology; Male; Medical management aid. Diagnosis aid; Medical sciences; Nerve Net; Problem solving, game playing; Prognosis; Reference Values; Reproducibility of Results; Time Factors; Error estimation; System performance; Computer simulation; Diabetes mellitus; Prediction; Decision aid; Network architecture; Neural network; Hypoglycemia; Learning algorithm; Medical application
• ISSN: 0899-7667; 1530-888X
• DOI: 10.1162/089976698300017566

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.