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Title: An Agent Based Decision Support Framework for Healthcare Policy, Augmented with Stateful Genetic Programming
Authors: Laskowski, Marek
Supervisor: McLeod, Robert D. (ECE) Friesen, Marcia (ECE)
Examining Committee: Alfa, Attahiru (ECE) Anderson, John (Computer Science) Paranjape, Raman (University of Regina)
Graduation Date: February 2011
Keywords: Agent Based Model
Machine Learning
Genetic Programming
Emergency Department
Decision Support System
Learning Control System
Issue Date: 31-Jan-2011
Citation: S. Mukhi and M. Laskowski, “Agent-based simulation of emergency departments with patient diversion,” in Electronic Healthcare, D. Weerasinghe, Ed. Berlin: Springer, 2009, pp. 25-37.
M. Laskowski, B. Demianyk, M.R. Friesen and R.D. McLeod, “Modeling an RFID tracking system in an emergency department”, presented at the IEEE Workshop on Healthcare Management, Venice, Italy, 2010
] M. Laskowski, R.D. McLeod, M.R. Friesen, B.W. Podaima, and A.S. Alfa, “Models of emergency departments for reducing patient waiting times”, PLoS ONE, vol. 4, no. 7: e6127, 2009. [Online]. Available: doi:10.1371/journal.pone.0006127 2009.
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 modeling (ABM) approach was used to simulate EDs, designed to be tuneable 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 modeling 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.
Appears in Collection(s):FGS - Electronic Theses & Dissertations (Public)

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