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.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
