Linked Life Data

9573413

Source:http://linkedlifedata.com/resource/pubmed/id/9573413

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
1998-6-4
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
May
pubmed:issn
0899-7667
pubmed:author
pubmed:issnType
Print
pubmed:day
15
pubmed:volume
10
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
941-53
pubmed:dateRevised
2011-11-17
pubmed:meshHeading
pubmed:year
1998
pubmed:articleTitle
Predictive neural networks for learning the time course of blood glucose levels from the complex interaction of counterregulatory hormones.
pubmed:affiliation
Abteilung Klinische Endokrinologie, Medizinische Hochschule Hannover, Hannover, Germany.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't