Dynamic updating in DIAS-NIDDM and DIAS causal probabilistic networks.
IEEE Trans Biomed Eng
; 46(2): 158-68, 1999 Feb.
Article
em En
| MEDLINE
| ID: mdl-9932337
ABSTRACT
Diabetes advisory system (DIAS) is a decision support system, which has been developed to provide advice on the amount of insulin injected by subjects with insulin-dependent diabetes mellitus (IDDM). DIAS employs a temporal causal probabilistic network (CPN) to implement a stochastic model of carbohydrate metabolism. The CPN network has recently been extended to provide also advice to subjects with noninsulin-dependent diabetes mellitus (NIDDM). However, due to increased complexity and size of the extended CPN the calculations became unfeasible. The CPN network was, therefore, simplified and a novel approach employed to generate conditional probability tables. The principles of dynamic CPN's were adopted and, in combination with the method of conditioning, learning, and forecasting, were implemented in a time- and memory-efficient way. An evaluation using experimental data was carried out to compare the original and revised DIAS implementations employing data collected by patients with IDDM, and to assess the a posteriori identifiability of model parameters in patients with NIDDM.
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Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Redes Neurais de Computação
/
Sistemas de Apoio a Decisões Clínicas
/
Diabetes Mellitus Tipo 2
Tipo de estudo:
Health_economic_evaluation
/
Prognostic_studies
Limite:
Adult
/
Female
/
Humans
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Male
/
Middle aged
Idioma:
En
Revista:
IEEE Trans Biomed Eng
Ano de publicação:
1999
Tipo de documento:
Article
País de afiliação:
Reino Unido