Graph-Grammar Assistance for Automated Generation of Influence Diagrams
This work addresses the difficulty of composing relevance diagrams for decision modeling in medicine, offering a domain-specific solution that is incremental in nature.
The paper tackled the challenge of modeling complex dilemmas in decision analysis by developing a graph-grammar production system that uses inherent interrelationships among medical terms to facilitate the creation of influence diagrams, resulting in an automated approach for generating these diagrams in medical domains.
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.