RESEARCH ARTICLE
Decision-Oriented Multi-Outcome Modeling for Anesthesia Patients
Zhibin Tan1, Romeo Kaddoum2, Le Yi Wang*, 1, Hong Wang2
Article Information
Identifiers and Pagination:
Year: 2010Volume: 4
First Page: 113
Last Page: 122
Publisher ID: TOBEJ-4-113
DOI: 10.2174/1874120701004010113
Article History:
Received Date: 17/2/2010Revision Received Date: 13/5/2010
Acceptance Date: 18/5/2010
Electronic publication date: 9/7/2010
Collection year: 2010
open-access license: This is an open access article licensed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0/) which permits unrestricted, non-commercial use, distribution and reproduction in any medium, provided the work is properly cited.
Abstract
Anesthesia drugs have impact on multiple outcomes of an anesthesia patient. Most typical outcomes include anesthesia depth, blood pressures, heart rates, etc. Traditional diagnosis and control in anesthesia focus on a one-drug-one-outcome scenario. This paper studies the problem of real-time modeling for monitoring, diagnosing, and predicting multiple outcomes of anesthesia patients. It is shown that consideration of multiple outcomes is necessary and beneficial for anesthesia managements. Due to limited real-time data, real-time modeling in multi-outcome modeling requires low-complexity model strucrtures. This paper introduces a method of decision-oriented modeling that significantly reduces the complexity of the problem. The method employs simplified and combined model functions in a Wiener structure to contain model complexity. The ideas of drug impact prediction and reachable sets are introduced for utility of the models in diagnosis, outcome prediction, and decision assistance. Clinical data are used to evaluate the effectiveness of the method.