Stochastic control with graphical models: the influence view approach

Paolo Magni, Riccardo Bellazzi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Markov decision processes (MDPs) allow to represent a wide class of problems in medical decision making and control. The complexity of the algorithms used to search the best policy of a MDP is directly related with the dimensionality of the state space. A careful structuring of the state space is hence an important task in the MDP specification. Graphical models are particularly appealing to cope with this task. In this paper we will describe a novel graphical formalism for MDP knowledge acquisition called Influence View (IV). An IV is a directed acyclic graph that depicts the probabilistic relationships between the problem state variables in a generic time transition; additional variables, called event variables, may be added, in order to describe the conditional independencies between state variables. By using the specified conditional independence structure, an IV may allow a parsimonious specification of a MDP. The authors have applied this methodology to the GVHD prophylaxis after Bone Marrow Transplantation.

Original languageEnglish
Title of host publicationAnnual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings
PublisherIEEE
Pages1670-1671
Number of pages2
Volume4
Publication statusPublished - 1996
EventProceedings of the 1996 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 4 (of 5) - Amsterdam, Neth
Duration: Oct 31 1996Nov 3 1996

Other

OtherProceedings of the 1996 18th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. Part 4 (of 5)
CityAmsterdam, Neth
Period10/31/9611/3/96

ASJC Scopus subject areas

  • Bioengineering

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