| Literature DB >> 10566369 |
Abstract
A decision analytic model represents uncertainties as probability distributions. These distributions are hard to assess especially for large and dynamic models. We propose an integrated framework that facilitates elicitation of the relevant probability distributions for dynamic decision models from the domain experts. The experts usually use some judgmental heuristics to aid probability assessments; the resulting distributions may be proned to cognitive biases. Our framework aims to minimize the effects of these biases and to improve the quality of decisions made. We have implemented a prototype system of the framework and evaluated its effectiveness via a case study in the follow-up management of colorectal cancer patients after curative surgery. Preliminary results demonstrate the practical promise of the framework.Entities:
Mesh:
Year: 1999 PMID: 10566369 PMCID: PMC2232553
Source DB: PubMed Journal: Proc AMIA Symp ISSN: 1531-605X