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ThinkMind // International Journal On Advances in Software, volume 2, number 1, 2009 // View article soft_v2_n1_2009_7


A Method for Automatically Eliciting node Weights in a Hierarchical Knowledge-Based Structure for Reasoning with Uncertainty

Authors:
Sherif Hegazy
C. D. Buckingham

Keywords: Clinical Decision Support Systems; Mental Health; Risk Screening; Hierarchical Knowledge; Decision Trees; Mathematical Modelling.

Abstract:
Hierarchical knowledge structures are frequently used within clinical decision support systems as part of the model for generating intelligent advice. The nodes in the hierarchy inevitably have varying influence on the decision-making processes, which needs to be reflected by parameters. If the model has been elicited from human experts, it is not feasible to ask them to estimate the parameters because there will be so many in even moderately-sized structures. This paper describes how the parameters could be obtained from data instead, using only a small number of cases. The original method [1] is applied to a particular web-based clinical decision support system called GRiST, which uses its hierarchical knowledge to quantify the risks associated with mental-health problems. The knowledge was elicited from multidisciplinary mental-health practitioners but the tree has several thousand nodes, all requiring an estimation of their relative influence on the assessment process. The method described in the paper shows how they can be obtained from about 200 cases instead. It greatly reduces the experts’ elicitation tasks and has the potential for being generalized to similar knowledge-engineering domains where relative weightings of node siblings are part of the parameter space.

Pages: 76 to 83

Copyright: Copyright (c) to authors, 2009. Used with permission.

Publication date: June 7, 2009

Published in: journal

ISSN: 1942-2628

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