Optimal Decision Explanation by Extracting Regularity Patterns

When solving decision-making problems with modern graphical models like influence diagrams, we obtain the decision tables with optimal decision alternatives. For real-life clinical problems, these tables are often extremely large, hindering the understanding of the reasons behind their content. KBM2L lists are new structures that simultaneously minimise memory storage space of these tables, and search for a better knowledge organisation. In this paper, we study the application of KBM2L lists in finding and thoroughly studying the optimal treatments for gastric nonHodgkin lymphoma. This is a difficult clinical problem, mainly because of the uncertainties involved. The resultant lists provide high-level explanations of optimal treatments for the disease, and are also able to find relationships between groups of variables and treatments.

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