End-User Construction of Influence Diagrams for Bayesian Statistics
This work addresses a domain-specific problem for physicians by providing a tool to simplify the use of Bayesian statistics in clinical trial interpretation, though it appears incremental as it builds on existing influence diagram concepts with a user-focused interface.
The paper tackles the difficulty end users face in interpreting and manipulating influence diagrams for Bayesian statistical models by introducing a user-based architecture that enables them to create and manipulate these representations, specifically applied to physicians interpreting two-arm parallel randomized clinical trials (TAPRCT) with an implemented system called THOMAS.
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.