Drug delivery optimization through Bayesian networks: An application to erythropoietin therapy in uremic anemia

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Abstract

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.

Original languageEnglish
Pages (from-to)274-293
Number of pages20
JournalComputers and Biomedical Research
Volume26
Issue number3
DOIs
Publication statusPublished - 1993

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Erythropoietin
Anemia
Pharmaceutical Preparations
Erythropoiesis
Hemoglobins
Therapeutics

ASJC Scopus subject areas

  • Medicine (miscellaneous)

Cite this

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abstract = "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.",
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