Linked Life Data

9179838

Source:http://linkedlifedata.com/resource/pubmed/id/9179838

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
1997-8-4
pubmed:abstractText
An intelligent decision support system for the analysis of home monitoring data and therapy planning in gestational diabetes is presented. The paper describes the integration of qualitative and quantitative reasoning modules within the DIABNET advisory system. The system kernel is a qualitative model of the physiological processes involved in the glucose metabolism of this type of diabetic patient. A causal probabilistic network (CPN) has been used to represent the qualitative model in order to manage uncertain and missing monitoring data. The DIABNET inputs are the patient's available ambulatory monitoring data, and the output is a dietary and insulin therapy adjustment that includes initiation of insulin therapy, quantitative insulin dose changes and qualitative diet and schedule modifications. Over periods of up to seven days, monitoring data are analysed by the CPN to detect any diet or insulin therapy items which may require modification. The qualitative insulin needs are translated into a quantitative proposal in line with the characteristics of the patient and the modification strategies usually used by doctors. The first evaluation of the system has been accomplished, the encouraging results of which are also presented.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
0307-7640
pubmed:author
pubmed:issnType
Print
pubmed:volume
21
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
359-74
pubmed:dateRevised
2011-11-17
pubmed:meshHeading
pubmed:articleTitle
DIABNET: a qualitative model-based advisory system for therapy planning in gestational diabetes.
pubmed:affiliation
Grupo de Bioingeniería y Telemedicina, ETSI de Telecommunicatión, Unversidad Politécnica de Madrid, Spain.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't