Cytotoxic chemotherapy monitoring using stochastic simulation on graphical models

Riccardo Bellazzi, Carlo Berzuini, Silvana Quaglini, David Spiegelhalter, Mark Leaning

Research output: Other contributionpeer-review


This paper describes a Bayes Network approach to the modelling of growth or response curves, with application to the monitoring of cytotoxicity in breast-cancer patients under chemotherapy cycles. The approach uses experience of past cycles of therapy of the patient at hand to perform an adaptive adjustment of the parameters of a patient’s specific model of the toxicity evolution. This adaptive process allows more accurate patient-specific predictions, and hence a more rational dosage planning. We use a stochastic simulation algorithm, called Gibbs sampling, to perform the necessary inference calculations on the graphical model. We also describe result obtained on real data, using our newly developed GAMEES program for Gibbs sampling on Bayesian graphical models.
Original languageEnglish
PublisherSpringer Nature
Number of pages12
Publication statusPublished - 1991

Publication series

Name Lecture Notes in Medical Informatics


  • Graphical model
  • Gibbs sampling
  • Stochastic simulation
  • Predictive distribution
  • Stochastic simulation algorithm


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