Jove
Visualize
Contact Us

Related Experiment Videos

Representation of preferences in decision-support systems.

B R Farr1, R D Schachter

  • 1Section on Medical Informatics, Stanford University.

Proceedings. Symposium on Computer Applications in Medical Care
|January 1, 1991
PubMed
Summary

Decision-theoretic preference models improve decision-support systems by explicitly capturing expert preferences. This study presents an efficient method to automatically refine these models by simulating expert decisions.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Accuracy and efficiency of an automated system for calculating APACHE II scores in an intensive care unit.

Proceedings : a conference of the American Medical Informatics Association. AMIA Fall Symposium·1997
Same author

The design and implementation of a ventilator-management advisor.

Artificial intelligence in medicine·1993
Same author

Representation of preferences in decision-support systems.

Computers and biomedical research, an international journal·1992
Same author

Localization of significant coronary arterial narrowings using body surface potential mapping during exercise stress testing.

The American journal of cardiology·1987
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Decision Analysis

Background:

  • Decision-support systems often rely on implicit expert preferences.
  • Explicitly represented preferences in decision-theoretic models offer significant advantages.

Purpose of the Study:

  • To describe the advantages of decision-theoretic preference models.
  • To present an accurate and efficient method for assessing and refining expert preferences within these models.

Main Methods:

  • Simulating domain-specific decisions for experts.
  • Inferring expert preferences from choices made in simulated scenarios.
  • Automatically refining preference models using inferred information.

Main Results:

Related Experiment Videos

  • Developed an efficient method for determining expert preferences.
  • Enabled automatic refinement of decision-theoretic preference models.
  • Demonstrated accuracy in preference assessment through simulated decisions.

Conclusions:

  • Explicit decision-theoretic preference models enhance decision-support systems.
  • The proposed method simplifies the creation and refinement of these models.
  • Automated preference assessment leads to more accurate and effective decision support.