Graph-Grammar Assistance for Automated Generation of Influence Diagrams

TL;DR

This paper introduces a graph-grammar system that leverages domain-specific semantic classifications to automate the creation of influence diagrams for complex decision models, especially in medical contexts.

Contribution

It presents a novel graph-grammar approach that uses medical concept classifications to assist in generating influence diagrams for decision analysis.

Findings

Automates influence diagram generation using domain-specific patterns.

Reduces modeling complexity for medical decision problems.

Demonstrates effectiveness in medical decision modeling.

Abstract

One of the most difficult aspects of modeling complex dilemmas in decision-analytic terms is composing a diagram of relevance relations from a set of domain concepts. Decision models in domains such as medicine, however, exhibit certain prototypical patterns that can guide the modeling process. Medical concepts can be classified according to semantic types that have characteristic positions and typical roles in an influence-diagram model. We have developed a graph-grammar production system that uses such inherent interrelationships among medical terms to facilitate the modeling of medical decisions.