Abstract
Most medical decision problems are exceedingly complex and contain a large number of variables. Abstraction facilitates the process of building a decision model by allowing a model builder to work at a level of detail that he is most comfortable with; it is also useful in time-critical situations or when there is insufficient data to support complete specification of probabilities of the uncertain events. In this paper, we identify and formalize abstraction and refinement operations commonly used in model construction. We illustrate the use of these mechanisms with an example on the follow-up management of colorectal cancer patients after surgery.
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Selected References
These references are in PubMed. This may not be the complete list of references from this article.
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