End-User Construction of Influence Diagrams for Bayesian Statistics
  Influence diagrams are ideal knowledge representations for Bayesian
statistical models. However, these diagrams are difficult for end users to
interpret and to manipulate. We present a user-based architecture that enables
end users to create and to manipulate the knowledge representation. We use the
problem of physicians' interpretation of two-arm parallel randomized clinical
trials (TAPRCT) to illustrate the architecture and its use. There are three
primary data structures. Elements of statistical models are encoded as
subgraphs of a restricted class of influence diagram. The interpretations of
those elements are mapped into users' language in a domain-specific, user-based
semantic interface, called a patient-flow diagram, in the TAPRCT problem.
Pennitted transformations of the statistical model that maintain the semantic
relationships of the model are encoded in a metadata-state diagram, called the
cohort-state diagram, in the TAPRCT problem. The algorithm that runs the system
uses modular actions called construction steps. This framework has been
implemented in a system called THOMAS, that allows physicians to interpret the
data reported from a TAPRCT.
