Linked Life Data

10566369

Source:http://linkedlifedata.com/resource/pubmed/id/10566369

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2000-2-1
pubmed:abstractText
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.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1531-605X
pubmed:author
pubmed:issnType
Print
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
301-5
pubmed:dateRevised
2008-11-20
pubmed:meshHeading
pubmed:year
1999
pubmed:articleTitle
PROBES: a framework for probability elicitation from experts.
pubmed:affiliation
Department of Computer Science, School of Computing, National University of Singapore. aikhiang@hotmail.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't