Uncertainty in Artificial Intelligence
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Assessment, Criticism and Improvement of Imprecise Subjective Probabilities for a Medical Expert System
David Spiegelhalter, Rodney Franklin, Kate Bull
Abstract:
Three paediatric cardiologists assessed nearly 1000 imprecise subjective conditional probabilities for a simple belief network representing congenital heart disease, and the quality of the assessments has been measured using prospective data on 200 babies. Quality has been assessed by a Brier scoring rule, which decomposes into terms measuring lack of discrimination and reliability. The results are displayed for each of 27 diseases and 24 questions, and generally the assessments are reliable although there was a tendency for the probabilities to be too extreme. The imprecision allows the judgements to be converted to implicit samples, and by combining with the observed data the probabilities naturally adapt with experience. This appears to be a practical procedure even for reasonably large expert systems.
Keywords: null
Pages: 285-294
PS Link:
PDF Link: /papers/89/p285-spiegelhalter.pdf
BibTex:
@INPROCEEDINGS{Spiegelhalter89,
AUTHOR = "David Spiegelhalter and Rodney Franklin and Kate Bull",
TITLE = "Assessment, Criticism and Improvement of Imprecise Subjective Probabilities for a Medical Expert System",
BOOKTITLE = "Uncertainty in Artificial Intelligence 5 Annual Conference on Uncertainty in Artificial Intelligence (UAI-89)",
PUBLISHER = "Elsevier Science",
ADDRESS = "Amsterdam, NL",
YEAR = "1989",
PAGES = "285--294"
}


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