Document Detail

Drug delivery optimization through Bayesian networks: an application to erythropoietin therapy in uremic anemia.
MedLine Citation:
PMID:  8325006     Owner:  NLM     Status:  MEDLINE    
This paper describes how Bayesian networks can be used in combination with compartmental models to plan recombinant human erythropoietin delivery in the treatment of anemia of chronic uremic patients. Past measurements of hemoglobin concentration in a patient during the therapy can be exploited to adjust the parameters of a compartmental model of erythropoiesis. This adaptive process provides more accurate patient-specific predictions, and hence a more rational dosage planning. Inferences are performed by using a stochastic simulation algorithm called Gibbs sampling. We describe a drug delivery optimization protocol based on our approach. Some results obtained on real data are presented.
R Bellazzi
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Publication Detail:
Type:  Case Reports; Journal Article; Research Support, Non-U.S. Gov't    
Journal Detail:
Title:  Computers and biomedical research, an international journal     Volume:  26     ISSN:  0010-4809     ISO Abbreviation:  Comput. Biomed. Res.     Publication Date:  1993 Jun 
Date Detail:
Created Date:  1993-08-10     Completed Date:  1993-08-10     Revised Date:  2006-11-15    
Medline Journal Info:
Nlm Unique ID:  0100331     Medline TA:  Comput Biomed Res     Country:  UNITED STATES    
Other Details:
Languages:  eng     Pagination:  274-93     Citation Subset:  IM    
Dipartimento di Informatica e Sistemistica, Universitá di Pavia, Italy.
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MeSH Terms
Anemia / drug therapy*,  etiology
Bayes Theorem*
Chronic Disease
Computer Simulation*
Costs and Cost Analysis
Drug Administration Schedule
Drug Therapy, Computer-Assisted*
Erythropoiesis / physiology
Erythropoietin / administration & dosage*,  economics
Hemoglobins / analysis
Models, Biological*
Uremia / complications*
Reg. No./Substance:
0/Hemoglobins; 11096-26-7/Erythropoietin

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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