A methodology for generating computer-based explanations of decision-theoretic advice.
Decision analysis is an appealing methodology with which to provide decision support to the practicing physician. However, its use in the clinical setting is impeded because computer-based explanations of decision-theoretic advice are difficult to generate without resorting to mathematical arguments. Nevertheless, human decision analysts generate useful and intuitive explanations based on decision trees. To facilitate the use of decision theory in a computer-based decision support system, the authors developed a computer program that uses symbolic reasoning techniques to generate nonquantitative explanations of the results of decision analyses. A combined approach has been implemented to explain the differences in expected utility among branches of a decision tree. First, the mathematical relationships inherent in the structure of the tree are used to find any asymmetries in tree structure or inequalities among analogous decision variables that are responsible for a difference in expected utility. Next, an explanation technique is selected and applied to the most significant variables, creating a symbolic expression that justifies the decision. Finally, the symbolic expression is converted to English-language text, thereby generating an explanation that justifies the desirability of the choice with the greater expected utility. The explanation does not refer to mathematical formulas, nor does it include probability or utility values. The results suggest that explanations produced by a combination of decision analysis and symbolic processing techniques may be more persuasive and acceptable to clinicians than those produced by either technique alone.
Langlotz
C P
CP
Medical Computer Science, Stanford University School of Medicine, California 94305-5479.
Shortliffe
E H
EH
Fagan
L M
LM
D001185:Artificial Intelligence; D003658:Decision Making, Computer-Assisted; D003663:Decision Trees; D006801:Humans