An Agent Based Decision Support Framework for Healthcare Policy, Augmented with Stateful Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4221

  author =       "Marek Laskowski",
  title =        "An Agent Based Decision Support Framework for
                 Healthcare Policy, Augmented with Stateful Genetic
  school =       "Department of Electrical and Computer Engineering,
                 Faculty of Engineering, University of Manitoba",
  year =         "2010",
  address =      "Winnipeg, Manitoba, Canada",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, agent-based
  URL =          "",
  URL =          "",
  URL =          "",
  size =         "352 pages",
  abstract =     "This research addresses the design and development of
                 a decision support tool to provide healthcare policy
                 makers with insights and feedback when evaluating
                 proposed patient flow and infection mitigation and
                 control strategies in the emergency department (ED). An
                 agent-based modelling (ABM) approach was used to
                 simulate EDs, designed to be tunable to specific
                 parameters related to specification of topography,
                 agent characteristics and behaviours, and the
                 application in question. In this way, it allows for the
                 user to simulate various what-if scenarios related to
                 infection spread and patient flow, where such policy
                 questions may otherwise be left best intent open loop
                 in practice. Infection spread modelling and patient
                 flow modeling have been addressed by mathematical and
                 queueing models in the past; however, the application
                 of an ABM approach at the level of an institution is
                 novel. A conjecture of this thesis is that such a tool
                 should be augmented with Machine Learning (ML)
                 technology to assist in performing optimization or
                 search in which patient flow and infection spread are
                 signals or variables of interest. Therefore this work
                 seeks to design and demonstrate a decision support tool
                 with ML capability for optimizing ED processes. The
                 primary contribution of this thesis is the development
                 of a novel, flexible, and tuneable framework for
                 spatial, human-scale ABM in the context of a decision
                 support tool for healthcare policy relating to
                 infection spread and patient flow within EDs . The
                 secondary contribution is the demonstration of the
                 utility of ML for automatic policy generation with
                 respect to the ABM tool. The application of ML to
                 automatically generate healthcare policy in concert
                 with an ABM is believed to be novel and of emerging
                 practical importance. The tertiary contribution is the
                 development and testing of a novel heuristic specific
                 to the ML paradigm used: Genetic Programming (GP). This
                 heuristic aids learning tasks performed in conjunction
                 with ABMs for healthcare policy. The primary
                 contribution is clearly demonstrated within this
                 thesis. The others are of a more difficult nature; the
                 groundwork has been laid for further work in these
                 areas that are likely to remain open for the
                 foreseeable future.",

Genetic Programming entries for Marek Laskowski