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Systems Engineering to Improve Traumatic Brain Injury Care in the Military Health System Workshop Summary (2009) / Chapter Skim
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Appendix H: Definitions and Examples of Operational Systems Engineering Tools and Concepts
Pages 169-174

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From page 169...
... and their probabilistic interdependencies. Bayesian networks encode probabilistic relationships among variables and account for circumstances in which data are missing and can be used to discover causal relationships (e.g., the relationship between symptoms and diseases)
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From page 170...
... , fuzzy set theory is based on a graduated valuation of the degree of "membership" in various elements in a set (e.g., as a patient's screening test score increases, his or her degree of membership for a particular level of traumatic brain injury [TBI] severity rises or falls)
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From page 171...
... The memoryless property means that the conditional probability of the system being in any given state in the future depends only upon its present state and is independent of any past states. (More advanced types of Markov chains can include several past states in the transition probabilities, but are memoryless beyond that amount of history.)
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From page 172...
... For example, in a resource-location-allocation study, an MIP model was used to locate TBI treatment units in the Department of Veterans Affairs. The objective was to simultaneously determine optimal facility locations and the optimal assignment of patients to those facilities.
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From page 173...
... SDT provides a mathematical framework for assessing such decisions -- for quantifying the test's ability to discriminate and for determining the optimal threshold for calling a patient positive or negative. Simulation models are computer models that emulate the logic of a process and use randomly generated data whenever a chance or random event (e.g., develops TBI, passes test, time durations)
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From page 174...
... For example, Monte Carlo models have been used to analyze and optimize cancer screening decision processes. • Agent-based models are based on the idea of "agents" that r ­ epresent each autonomous or semi-autonomous decision maker who chooses his or her next action based on the current status of the surrounding environment.
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Key Terms

  • transition probabilities
  • decision trees
  • decision maker
  • traumatic brain
  • bayesian networks

  • brain injury
  • models below
  • decision processes
  • monte carlo
  • markov chains

  • markov decision
  • past states
  • fuzzy set
  • set theory
  • influence diagrams

  • bayesian decision
  • carlo models
  • mathematical framework
  • sensitivity analyses
  • screening tests

  • memoryless property
  • fuzzy logic
  • similar conceptually
  • competing alternatives
  • maximize performance

  • control models
  • true state
  • severity rises
  • alternative process
  • trees below


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